1
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Nussinov R, Yavuz BR, Jang H. Single cell spatial biology over developmental time can decipher pediatric brain pathologies. Neurobiol Dis 2024; 199:106597. [PMID: 38992777 DOI: 10.1016/j.nbd.2024.106597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/18/2024] [Accepted: 07/07/2024] [Indexed: 07/13/2024] Open
Abstract
Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
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2
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Fu YC, Das A, Wang D, Braun R, Yi R. scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis. Genome Biol 2024; 25:164. [PMID: 38915088 PMCID: PMC11197379 DOI: 10.1186/s13059-024-03299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 06/04/2024] [Indexed: 06/26/2024] Open
Abstract
Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell-cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell-cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.
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Affiliation(s)
- Yuheng C Fu
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Arpan Das
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Dongmei Wang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Rosemary Braun
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA.
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, 60208, USA.
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA.
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL, 60208, USA.
| | - Rui Yi
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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3
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Rahimi A, Vale-Silva LA, Fälth Savitski M, Tanevski J, Saez-Rodriguez J. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nat Commun 2024; 15:4994. [PMID: 38862466 PMCID: PMC11167014 DOI: 10.1038/s41467-024-48868-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 05/14/2024] [Indexed: 06/13/2024] Open
Abstract
Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.
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Affiliation(s)
- Arezou Rahimi
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany
- Cellzome GmbH, GlaxoSmithKline, Heidelberg, Germany
| | | | | | - Jovan Tanevski
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany.
- Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia.
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital, Heidelberg, Germany.
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4
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Steiner D, Sultan L, Sullivan T, Liu H, Zhang S, LeClerc A, Alekseyev YO, Liu G, Mazzilli SA, Zhang J, Rieger-Christ K, Burks EJ, Beane J, Lenburg ME. Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597993. [PMID: 38915565 PMCID: PMC11195124 DOI: 10.1101/2024.06.07.597993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI+ compared with VI- tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42×10-6) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI- LUAD (HR=2.76; p=0.003). To determine our VI predictor's robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2×10-16) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.
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Affiliation(s)
- Dylan Steiner
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Lila Sultan
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Travis Sullivan
- Department of Translational Research, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Hanqiao Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sherry Zhang
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Ashley LeClerc
- Boston University Microarray and Sequencing Resource Core Facility, Boston, MA, USA
| | - Yuriy O Alekseyev
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Gang Liu
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sarah A Mazzilli
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jiarui Zhang
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Kimberly Rieger-Christ
- Department of Translational Research, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Eric J Burks
- Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jennifer Beane
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA, Department of Pathology and Laboratory Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
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5
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Brown EL. Charting spatially resolved cell states with CytoSPACE. Nat Rev Cancer 2024:10.1038/s41568-024-00713-7. [PMID: 38858510 DOI: 10.1038/s41568-024-00713-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Affiliation(s)
- Erin L Brown
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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6
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Liu H, Gao J, Feng M, Cheng J, Tang Y, Cao Q, Zhao Z, Meng Z, Zhang J, Zhang G, Zhang C, Zhao M, Yan Y, Wang Y, Xue R, Zhang N, Li H. Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma. Cancer Cell 2024; 42:1067-1085.e11. [PMID: 38759655 DOI: 10.1016/j.ccell.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 03/21/2024] [Accepted: 04/26/2024] [Indexed: 05/19/2024]
Abstract
In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.
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MESH Headings
- Humans
- Melanoma/genetics
- Melanoma/immunology
- Melanoma/pathology
- Epithelial-Mesenchymal Transition/genetics
- Skin Neoplasms/genetics
- Skin Neoplasms/immunology
- Skin Neoplasms/pathology
- Antigens, Differentiation, Myelomonocytic/metabolism
- Antigens, Differentiation, Myelomonocytic/genetics
- Antigens, CD/metabolism
- Antigens, CD/genetics
- Neoplasm Invasiveness
- Apolipoproteins E/genetics
- Macrophages/immunology
- Macrophages/metabolism
- Male
- Female
- Receptor, IGF Type 1/genetics
- Receptor, IGF Type 1/metabolism
- Tumor Microenvironment/immunology
- Tumor Microenvironment/genetics
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Gene Expression Regulation, Neoplastic
- Spatial Analysis
- Middle Aged
- Prognosis
- Disease Progression
- Aged
- Receptors, Cell Surface
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Affiliation(s)
- Hengkang Liu
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China
| | - Jiawen Gao
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China; Institute of Photomedicine and Department of Phototherapy at Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai 200443, China
| | - Mei Feng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Jinghui Cheng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Yuchen Tang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Qi Cao
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ziji Zhao
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ziqiao Meng
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China
| | - Jiarui Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Guohong Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Chong Zhang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Mingming Zhao
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Yicen Yan
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Yang Wang
- National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China
| | - Ruidong Xue
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China.
| | - Ning Zhang
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; School of Basic Medical Sciences, International Cancer Institute, Peking University, Beijing 100191, China; Yunnan Baiyao Group, Kunming 650500, China.
| | - Hang Li
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University First Hospital, Beijing 100191, China; National Clinical Research Center for Skin and Immune Diseases, NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Peking University First Hospital, Beijing 100034, China; Yunnan Baiyao Group, Kunming 650500, China.
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7
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Drexler R, Khatri R, Sauvigny T, Mohme M, Maire CL, Ryba A, Zghaibeh Y, Dührsen L, Salviano-Silva A, Lamszus K, Westphal M, Gempt J, Wefers AK, Neumann JE, Bode H, Hausmann F, Huber TB, Bonn S, Jütten K, Delev D, Weber KJ, Harter PN, Onken J, Vajkoczy P, Capper D, Wiestler B, Weller M, Snijder B, Buck A, Weiss T, Göller PC, Sahm F, Menstel JA, Zimmer DN, Keough MB, Ni L, Monje M, Silverbush D, Hovestadt V, Suvà ML, Krishna S, Hervey-Jumper SL, Schüller U, Heiland DH, Hänzelmann S, Ricklefs FL. A prognostic neural epigenetic signature in high-grade glioma. Nat Med 2024; 30:1622-1635. [PMID: 38760585 PMCID: PMC11186787 DOI: 10.1038/s41591-024-02969-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/03/2024] [Indexed: 05/19/2024]
Abstract
Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.
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Affiliation(s)
- Richard Drexler
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cecile L Maire
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alice Ryba
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yahya Zghaibeh
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lasse Dührsen
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Amanda Salviano-Silva
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annika K Wefers
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julia E Neumann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Molecular Neurobiology Hamburg (ZMNH), University Hospital Hamburg Eppendorf, Hamburg, Germany
| | - Helena Bode
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, University Hospital Aachen, Aachen, Germany
| | - Daniel Delev
- Department of Neurosurgery, University Hospital Aachen, Aachen, Germany
- Department of Neurosurgery, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Katharina J Weber
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
- University Cancer Center (UCT) Frankfurt, Frankfurt am Main, Germany
| | - Patrick N Harter
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Frankfurt am Main, Germany
- Institute of Neuropathology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Julia Onken
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - David Capper
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University Munich, Munich, Germany
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
- Department of Neurology, University of Zürich, Zurich, Switzerland
| | - Berend Snijder
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Alicia Buck
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
- Department of Neurology, University of Zürich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
- Department of Neurology, University of Zürich, Zurich, Switzerland
| | - Pauline C Göller
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Felix Sahm
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Joelle Aline Menstel
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | - David Niklas Zimmer
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | | | - Lijun Ni
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Michelle Monje
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Dana Silverbush
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Volker Hovestadt
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mario L Suvà
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Saritha Krishna
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Shawn L Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | - Ulrich Schüller
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
- Translational Neurosurgery, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franz L Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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8
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Shi W, Zhang J, Huang S, Fan Q, Cao J, Zeng J, Wu L, Yang C. Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry. JACS AU 2024; 4:1723-1743. [PMID: 38818076 PMCID: PMC11134576 DOI: 10.1021/jacsau.4c00118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.
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Affiliation(s)
- Weixiong Shi
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jing Zhang
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
| | - Shanqing Huang
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Qian Fan
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiao Cao
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jun Zeng
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lingling Wu
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Chaoyong Yang
- Institute
of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry
and Nanomedicine, Renji Hospital, Shanghai
Jiao Tong University School of Medicine, Shanghai 200127, China
- The
MOE Key Laboratory of Spectrochemical Analysis & Instrumentation,
Discipline of Intelligent Instrument and Equipment, Department of
Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- State
Key Laboratory of Cellular Stress Biology, School of Life Sciences,
Faculty of Medicine and Life Sciences, Xiamen
University, Xiamen 361102, China
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9
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Fujiwara N, Kimura G, Nakagawa H. Emerging Roles of Spatial Transcriptomics in Liver Research. Semin Liver Dis 2024; 44:115-132. [PMID: 38574750 DOI: 10.1055/a-2299-7880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.
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Affiliation(s)
- Naoto Fujiwara
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Genki Kimura
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
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10
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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11
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Umbaugh DS, Nguyen NT, Smith SH, Ramachandran A, Jaeschke H. The p21 + perinecrotic hepatocytes produce the chemokine CXCL14 after a severe acetaminophen overdose promoting hepatocyte injury and delaying regeneration. Toxicology 2024; 504:153804. [PMID: 38614205 PMCID: PMC11108579 DOI: 10.1016/j.tox.2024.153804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/30/2024] [Accepted: 04/10/2024] [Indexed: 04/15/2024]
Abstract
Fifty percent of all acute liver failure (ALF) cases in the United States are due to acetaminophen (APAP) overdose. Assessment of canonical features of liver injury, such as plasma alanine aminotransferase activities are poor predictors of acute liver failure (ALF), suggesting the involvement of additional mechanisms independent of hepatocyte death. Previous work demonstrated a severe overdose of APAP results in impaired regeneration, the induction of senescence by p21, and increased mortality. We hypothesized that a discrete population of p21+ hepatocytes acquired a secretory phenotype that directly impedes liver recovery after a severe APAP overdose. Leveraging in-house human APAP explant liver and publicly available single-nuclei RNAseq data, we identified a subpopulation of p21+ hepatocytes enriched in a unique secretome of factors, such as CXCL14. Spatial transcriptomics in the mouse model of APAP overdose confirmed the presence of a p21+ hepatocyte population that directly surrounded the necrotic areas. In both male and female mice, we found a dose-dependent induction of p21 and persistent circulating levels of the p21-specific constituent, CXCL14, in the plasma after a severe APAP overdose. In parallel experiments, we targeted either the putative senescent hepatocytes with the senolytic drugs, dasatinib and quercetin, or CXCL14 with a neutralizing antibody. We found that targeting CXCL14 greatly enhanced liver recovery after APAP-induced liver injury, while targeting senescent hepatocytes had no effect. These data support the conclusion that the sustained induction of p21 in hepatocytes with persistent CXCL14 secretion are critical mechanistic events leading to ALF in mice and human patients.
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Affiliation(s)
- David S Umbaugh
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Nga T Nguyen
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Sawyer H Smith
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Anup Ramachandran
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Hartmut Jaeschke
- Department of Pharmacology, Toxicology & Therapeutics, University of Kansas Medical Center, Kansas City, KS, USA.
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12
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Khatri R, Machart P, Bonn S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 2024; 25:112. [PMID: 38689377 PMCID: PMC11061925 DOI: 10.1186/s13059-024-03251-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data. An unmet challenge in cell deconvolution is the scarcity of realistic training data and the domain shift often observed in synthetic training data. Here, we show that two novel deep neural networks with simultaneous consistency regularization of the target and training domains significantly improve deconvolution performance. Our algorithm, DISSECT, outperforms competing algorithms in cell fraction and gene expression estimation by up to 14 percentage points. DISSECT can be easily adapted to other biomedical data types, as exemplified by our proteomic deconvolution experiments.
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Affiliation(s)
- Robin Khatri
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Pierre Machart
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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13
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Stouffer KM, Trouvé A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Chen X, Rue M, Miller MI. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nat Commun 2024; 15:3530. [PMID: 38664422 PMCID: PMC11045777 DOI: 10.1038/s41467-024-47883-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France.
| | - Alain Trouvé
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Manjari Anant
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, Penn State University, College of Medicine, State College, PA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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14
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Sadeghirad H, Yaghoubi Naei V, O'Byrne K, Warkiani ME, Kulasinghe A. In situ characterization of the tumor microenvironment. Curr Opin Biotechnol 2024; 86:103083. [PMID: 38382325 DOI: 10.1016/j.copbio.2024.103083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 12/07/2023] [Accepted: 01/30/2024] [Indexed: 02/23/2024]
Abstract
The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.
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Affiliation(s)
- Habib Sadeghirad
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Vahid Yaghoubi Naei
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia; School of Biomedical Engineering, University of Technology Sydney, NSW, Australia
| | - Ken O'Byrne
- Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Majid E Warkiani
- School of Biomedical Engineering, University of Technology Sydney, NSW, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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15
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Yin W, Wan Y, Zhou Y. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief Bioinform 2024; 25:bbae130. [PMID: 38557675 PMCID: PMC10982953 DOI: 10.1093/bib/bbae130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
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Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - You Wan
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
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16
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Ma R, Sun ED, Donoho D, Zou J. Principled and interpretable alignability testing and integration of single-cell data. Proc Natl Acad Sci U S A 2024; 121:e2313719121. [PMID: 38416677 DOI: 10.1073/pnas.2313719121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 01/23/2024] [Indexed: 03/01/2024] Open
Abstract
Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features. SMAI provides a statistical test to robustly assess the alignability between datasets to avoid misleading inference and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.
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Affiliation(s)
- Rong Ma
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - David Donoho
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
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17
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Han G, Sinjab A, Rahal Z, Lynch AM, Treekitkarnmongkol W, Liu Y, Serrano AG, Feng J, Liang K, Khan K, Lu W, Hernandez SD, Liu Y, Cao X, Dai E, Pei G, Hu J, Abaya C, Gomez-Bolanos LI, Peng F, Chen M, Parra ER, Cascone T, Sepesi B, Moghaddam SJ, Scheet P, Negrao MV, Heymach JV, Li M, Dubinett SM, Stevenson CS, Spira AE, Fujimoto J, Solis LM, Wistuba II, Chen J, Wang L, Kadara H. An atlas of epithelial cell states and plasticity in lung adenocarcinoma. Nature 2024; 627:656-663. [PMID: 38418883 PMCID: PMC10954546 DOI: 10.1038/s41586-024-07113-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 01/24/2024] [Indexed: 03/02/2024]
Abstract
Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.
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Affiliation(s)
- Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zahraa Rahal
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anne M Lynch
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA
| | - Warapen Treekitkarnmongkol
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuejiang Liu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Alejandra G Serrano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiping Feng
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ke Liang
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaja Khan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Lu
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharia D Hernandez
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xuanye Cao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Camille Abaya
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lorena I Gomez-Bolanos
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Minyue Chen
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Edwin R Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Cascone
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Cardiovascular and Thoracic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Seyed Javad Moghaddam
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marcelo V Negrao
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven M Dubinett
- Department of Medicine, The University of California Los Angeles, Los Angeles, CA, USA
| | | | - Avrum E Spira
- Lung Cancer Initiative at Johnson & Johnson, Boston, MA, USA
- Section of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, USA
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luisa M Solis
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jichao Chen
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas Health Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
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18
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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19
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods 2024; 21:521-530. [PMID: 38366241 PMCID: PMC10927565 DOI: 10.1038/s41592-024-02171-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel C Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Timothy J Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Jonathan V Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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20
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Sun ED, Ma R, Navarro Negredo P, Brunet A, Zou J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat Methods 2024; 21:444-454. [PMID: 38347138 DOI: 10.1038/s41592-024-02184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/12/2024] [Indexed: 02/27/2024]
Abstract
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.
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Affiliation(s)
- Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Anne Brunet
- Department of Genetics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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21
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Kojima Y, Mii S, Hayashi S, Hirose H, Ishikawa M, Akiyama M, Enomoto A, Shimamura T. Single-cell colocalization analysis using a deep generative model. Cell Syst 2024; 15:180-192.e7. [PMID: 38387441 DOI: 10.1016/j.cels.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 03/06/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024]
Abstract
Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan; Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
| | - Shinji Mii
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Shuto Hayashi
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Haruka Hirose
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Kyoto 606-8507, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Masashi Akiyama
- Department of Dermatology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Atsushi Enomoto
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Teppei Shimamura
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
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22
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Bull JA, Mulholland EJ, Leedham SJ, Byrne HM. Extended correlation functions for spatial analysis of multiplex imaging data. BIOLOGICAL IMAGING 2024; 4:e2. [PMID: 38516631 PMCID: PMC10951806 DOI: 10.1017/s2633903x24000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 01/11/2024] [Accepted: 01/28/2024] [Indexed: 03/23/2024]
Abstract
Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
| | - Eoghan J. Mulholland
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
| | - Simon J. Leedham
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7DQ, UK
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23
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Gianopoulos I, Daskalopoulou SS. Macrophage profiling in atherosclerosis: understanding the unstable plaque. Basic Res Cardiol 2024; 119:35-56. [PMID: 38244055 DOI: 10.1007/s00395-023-01023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/01/2023] [Accepted: 11/01/2023] [Indexed: 01/22/2024]
Abstract
The development and rupture of atherosclerotic plaques is a major contributor to myocardial infarctions and ischemic strokes. The dynamic evolution of the plaque is largely attributed to monocyte/macrophage functions, which respond to various stimuli in the plaque microenvironment. To this end, macrophages play a central role in atherosclerotic lesions through the uptake of oxidized low-density lipoprotein that gets trapped in the artery wall, and the induction of an inflammatory response that can differentially affect the stability of the plaque in men and women. In this environment, macrophages can polarize towards pro-inflammatory M1 or anti-inflammatory M2 phenotypes, which represent the extremes of the polarization spectrum that include Mhem, M(Hb), Mox, and M4 populations. However, this traditional macrophage model paradigm has been redefined to include numerous immune and nonimmune cell clusters based on in-depth unbiased single-cell approaches. The goal of this review is to highlight (1) the phenotypic and functional properties of monocyte subsets in the circulation, and macrophage populations in atherosclerotic plaques, as well as their contribution towards stable or unstable phenotypes in men and women, and (2) single-cell RNA sequencing studies that have advanced our knowledge of immune, particularly macrophage signatures present in the atherosclerotic niche. We discuss the importance of performing high-dimensional approaches to facilitate the development of novel sex-specific immunotherapies that aim to reduce the risk of cardiovascular events.
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Affiliation(s)
- Ioanna Gianopoulos
- Division of Experimental Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada
| | - Stella S Daskalopoulou
- Division of Experimental Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada.
- Division of Internal Medicine, Department of Medicine, Faculty of Medicine and Health Sciences, McGill University Health Centre, McGill University, Montreal, Canada.
- Department of Medicine, Research Institute of the McGill University Health Centre, Glen Site, 1001 Decarie Boulevard, EM1.2210, Montreal, Quebec, H4A 3J1, Canada.
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24
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Fan J, Lu F, Qin T, Peng W, Zhuang X, Li Y, Hou X, Fang Z, Yang Y, Guo E, Yang B, Li X, Fu Y, Kang X, Wu Z, Han L, Mills GB, Ma X, Li K, Wu P, Ma D, Chen G, Sun C. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nat Genet 2023; 55:2175-2188. [PMID: 37985817 DOI: 10.1038/s41588-023-01570-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/16/2023] [Indexed: 11/22/2023]
Abstract
Cervical squamous cell carcinoma (CSCC) exhibits a limited response to immune-checkpoint blockade. Here we conducted a multiomic analysis encompassing single-cell RNA sequencing, spatial transcriptomics and spatial proteomics, combined with genetic and pharmacological perturbations to systematically develop a high-resolution and spatially resolved map of intratumoral expression heterogeneity in CSCC. Three tumor states (epithelial-cytokeratin, epithelial-immune (Epi-Imm) and epithelial senescence), recapitulating different stages of squamous differentiation, showed distinct tumor immune microenvironments. Bidirectional interactions between epithelial-cytokeratin malignant cells and immunosuppressive cancer-associated fibroblasts form an immune exclusionary microenvironment through transforming growth factor β pathway signaling mediated by FABP5. In Epi-Imm tumors, malignant cells interact with natural killer and T cells through interferon signaling. Preliminary analysis of samples from a cervical cancer clinical trial ( NCT04516616 ) demonstrated neoadjuvant chemotherapy induces a state transition to Epi-Imm, which correlates with pathological complete remission following treatment with immune-checkpoint blockade. These findings deepen the understanding of cellular state diversity in CSCC.
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Affiliation(s)
- Junpeng Fan
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Funian Lu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tianyu Qin
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenju Peng
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xucui Zhuang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yinuo Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Hou
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zixuan Fang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yunyi Yang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ensong Guo
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bin Yang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Fu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyan Kang
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zimeng Wu
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lili Han
- Department of Gynecology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Gordon B Mills
- Division of Oncological Sciences, Oregon Health and Sciences University, Portland, OR, USA
- Knight Cancer Institute, Portland, OR, USA
| | - Xiangyi Ma
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Kezhen Li
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Peng Wu
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Gynecological Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Ding Ma
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Gang Chen
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Chaoyang Sun
- Department of Gynecological Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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25
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Zhou Z, Zhong Y, Zhang Z, Ren X. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve. Nat Commun 2023; 14:7930. [PMID: 38040768 PMCID: PMC10692090 DOI: 10.1038/s41467-023-43600-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/14/2023] [Indexed: 12/03/2023] Open
Abstract
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
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Affiliation(s)
- Zixiang Zhou
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, 100871, Beijing, China
| | - Yunshan Zhong
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Zemin Zhang
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, 100871, Beijing, China
| | - Xianwen Ren
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China.
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26
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Wan X, Xiao J, Tam SST, Cai M, Sugimura R, Wang Y, Wan X, Lin Z, Wu AR, Yang C. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat Commun 2023; 14:7848. [PMID: 38030617 PMCID: PMC10687049 DOI: 10.1038/s41467-023-43629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023] Open
Abstract
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.
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Affiliation(s)
- Xiaomeng Wan
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Sindy Sing Ting Tam
- Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China
| | - Ryohichi Sugimura
- Li Ka Shing Faculty of Medicine, School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Yang Wang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Zhixiang Lin
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Angela Ruohao Wu
- Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China.
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Center for Aging Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
- Big Data Bio-Intelligence Lab, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543144. [PMID: 37398021 PMCID: PMC10312594 DOI: 10.1101/2023.05.31.543144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Daniel C. Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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28
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Benotmane JK, Kueckelhaus J, Will P, Zhang J, Ravi VM, Joseph K, Sankowski R, Beck J, Lee-Chang C, Schnell O, Heiland DH. High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq. Nat Commun 2023; 14:7432. [PMID: 37973846 PMCID: PMC10654577 DOI: 10.1038/s41467-023-43201-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Spatial resolution of the T cell repertoire is essential for deciphering cancer-associated immune dysfunction. Current spatially resolved transcriptomic technologies are unable to directly annotate T cell receptors (TCR). We present spatially resolved T cell receptor sequencing (SPTCR-seq), which integrates optimized target enrichment and long-read sequencing for highly sensitive TCR sequencing. The SPTCR computational pipeline achieves yield and coverage per TCR comparable to alternative single-cell TCR technologies. Our comparison of PCR-based and SPTCR-seq methods underscores SPTCR-seq's superior ability to reconstruct the entire TCR architecture, including V, D, J regions and the complementarity-determining region 3 (CDR3). Employing SPTCR-seq, we assess local T cell diversity and clonal expansion across spatially discrete niches. Exploration of the reciprocal interaction of the tumor microenvironmental and T cells discloses the critical involvement of NK and B cells in T cell exhaustion. Integrating spatially resolved omics and TCR sequencing provides as a robust tool for exploring T cell dysfunction in cancers and beyond.
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Affiliation(s)
- Jasim Kada Benotmane
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
| | - Jan Kueckelhaus
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
| | - Paulina Will
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
| | - Junyi Zhang
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
| | - Vidhya M Ravi
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
- Translational NeuroOncology Research Group, Medical Center-University of Freiburg, Freiburg, Germany
| | - Kevin Joseph
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany
- Translational NeuroOncology Research Group, Medical Center-University of Freiburg, Freiburg, Germany
- Center for NeuroModulation (NeuroModul), University of Freiburg, Freiburg, Germany
| | - Roman Sankowski
- Institute of Neuropathology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
| | - Catalina Lee-Chang
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Freiburg University, Freiburg, Germany
- Translational NeuroOncology Research Group, Medical Center-University of Freiburg, Freiburg, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany.
- Faculty of Medicine, Freiburg University, Freiburg, Germany.
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, Freiburg, Germany.
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- German Cancer Consortium (DKTK), partner site Freiburg, Freiburg, Germany.
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Iglesia MD, Jayasinghe RG, Chen S, Terekhanova NV, Herndon JM, Storrs E, Karpova A, Zhou DC, Al Deen NN, Shinkle AT, Lu RJH, Caravan W, Houston A, Zhao Y, Sato K, Lal P, Street C, Rodrigues FM, Southard-Smith AN, Targino da Costa ALN, Zhu H, Mo CK, Crowson L, Fulton RS, Wyczalkowski MA, Fronick CC, Fulton LA, Sun H, Davies SR, Appelbaum EL, Chasnoff SE, Carmody M, Brooks C, Liu R, Wendl MC, Oh C, Bender D, Cruchaga C, Harari O, Bredemeyer A, Lavine K, Bose R, Margenthaler J, Held JM, Achilefu S, Ademuyiwa F, Aft R, Ma C, Colditz GA, Ju T, Oh ST, Fitzpatrick J, Hwang ES, Shoghi KI, Chheda MG, Veis DJ, Chen F, Fields RC, Gillanders WE, Ding L. Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.565031. [PMID: 37961519 PMCID: PMC10634973 DOI: 10.1101/2023.10.31.565031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.
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Shireman JM, Cheng L, Goel A, Garcia DM, Partha S, Quiñones-Hinojosa A, Kendziorski C, Dey M. Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough? Front Oncol 2023; 13:1266397. [PMID: 37916170 PMCID: PMC10618006 DOI: 10.3389/fonc.2023.1266397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Spatial transcriptomics, the technology of visualizing cellular gene expression landscape in a cells native tissue location, has emerged as a powerful tool that allows us to address scientific questions that were elusive just a few years ago. This technological advance is a decisive jump in the technological evolution that is revolutionizing studies of tissue structure and function in health and disease through the introduction of an entirely new dimension of data, spatial context. Perhaps the organ within the body that relies most on spatial organization is the brain. The central nervous system's complex microenvironmental and spatial architecture is tightly regulated during development, is maintained in health, and is detrimental when disturbed by pathologies. This inherent spatial complexity of the central nervous system makes it an exciting organ to study using spatial transcriptomics for pathologies primarily affecting the brain, of which Glioblastoma is one of the worst. Glioblastoma is a hyper-aggressive, incurable, neoplasm and has been hypothesized to not only integrate into the spatial architecture of the surrounding brain, but also possess an architecture of its own that might be actively remodeling the surrounding brain. In this review we will examine the current landscape of spatial transcriptomics in glioblastoma, outline novel findings emerging from the rising use of spatial transcriptomics, and discuss future directions and ultimate clinical/translational avenues.
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Affiliation(s)
- Jack M. Shireman
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Lingxin Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amiti Goel
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | - Diogo Moniz Garcia
- Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States
| | - Sanil Partha
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Mahua Dey
- Department of Neurosurgery, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison (UW) Carbone Cancer Center, Madison, WI, United States
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31
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Zhou R, Yang G, Zhang Y, Wang Y. Spatial transcriptomics in development and disease. MOLECULAR BIOMEDICINE 2023; 4:32. [PMID: 37806992 PMCID: PMC10560656 DOI: 10.1186/s43556-023-00144-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
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Affiliation(s)
- Ran Zhou
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gaoxia Yang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Zhang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yuan Wang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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32
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Hou Y, Yao H, Lin JM. Recent advancements in single-cell metabolic analysis for pharmacological research. J Pharm Anal 2023; 13:1102-1116. [PMID: 38024859 PMCID: PMC10658044 DOI: 10.1016/j.jpha.2023.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/12/2023] [Accepted: 08/18/2023] [Indexed: 12/01/2023] Open
Abstract
Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology. Pharmacological research is being advanced by single-cell metabolic analysis, which offers a technique to identify variations in RNA, proteins, metabolites, and drug molecules in cells. In this review, the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized. High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods, such as droplet microfluidics, microchamber, open microfluidic probe, and digital microfluidics. They are used in tandem with variety of detection techniques, including optical imaging, Raman spectroscopy, electrochemical detection, RNA sequencing, and mass spectrometry, to evaluate single-cell metabolic changes in response to drug administration. The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis. These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway, therapeutic targets discovery, and in vitro disease model evaluation.
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Affiliation(s)
- Ying Hou
- Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Hongren Yao
- Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - Jin-Ming Lin
- Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, 100084, China
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33
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Sun ED, Ma R, Navarro Negredo P, Brunet A, Zou J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538326. [PMID: 37162839 PMCID: PMC10168375 DOI: 10.1101/2023.04.25.538326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Across eleven benchmark datasets, TISSUE provides well-calibrated prediction intervals for predicted expression values. Moreover it consistently reduces false discovery rates for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics, and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers. TISSUE is publicly available as a flexible wrapper method for existing spatial gene expression prediction methods to assist researchers with implementing uncertainty-aware analyses of spatial transcriptomics data.
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Affiliation(s)
- Eric D. Sun
- Department of Biomedical Data Science, Stanford University
| | - Rong Ma
- Department of Statistics, Stanford University
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | | | - Anne Brunet
- Department of Genetics, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University
- Glenn Center for the Biology of Aging, Stanford University
| | - James Zou
- Department of Biomedical Data Science, Stanford University
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Zhang Q, Jiang S, Schroeder A, Hu J, Li K, Zhang B, Dai D, Lee EB, Xiao R, Li M. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry. Nat Commun 2023; 14:4050. [PMID: 37422469 PMCID: PMC10329686 DOI: 10.1038/s41467-023-39895-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 07/03/2023] [Indexed: 07/10/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.
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Affiliation(s)
- Qihuang Zhang
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, QC, Canada.
| | - Shunzhou Jiang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Amelia Schroeder
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - Kejie Li
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA, 02142, USA
| | - Baohong Zhang
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA, 02142, USA
| | - David Dai
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Rui Xiao
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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35
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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Li X, Xiao C, Qi J, Xue W, Xu X, Mu Z, Zhang J, Li CY, Ding W. STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data. Nucleic Acids Res 2023:7177883. [PMID: 37224539 DOI: 10.1093/nar/gkad419] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.
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Affiliation(s)
- Xiangshang Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chunfu Xiao
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Juntian Qi
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | | | - Xinwei Xu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- College of Life Science, Peking University, Beijing 100871, China
| | - Zelin Mu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Jie Zhang
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chuan-Yun Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Wanqiu Ding
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Bioinformatics Core, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
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Stouffer KM, Trouv A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Miller MI. A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534622. [PMID: 37034802 PMCID: PMC10081224 DOI: 10.1101/2023.03.28.534622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.
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