1
|
Simard MA, Cabrera-Galvez C, Viteri S, Geist F, Reischmann N, Zühlsdorf M, Karachaliou N. Spatial profiling of METex14-altered NSCLC under tepotinib treatment: Shifting the immunosuppressive landscape. Neoplasia 2024; 57:101063. [PMID: 39366215 DOI: 10.1016/j.neo.2024.101063] [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: 05/28/2024] [Accepted: 09/25/2024] [Indexed: 10/06/2024]
Abstract
MET inhibitors have demonstrated efficacy in treating patients with non-small cell lung cancer (NSCLC) harboring METex14 skipping alterations. Advancements in spatial profiling technologies have unveiled the complex dynamics of the tumor microenvironment (TME), a crucial factor in cancer progression and therapeutic response. This study uses spatial profiling to investigate the effects of the MET inhibitor tepotinib on the TME in a case of locally advanced NSCLC with a METex14 skipping alteration. A patient with resectable stage IIIB NSCLC, unresponsive to neoadjuvant platinum-based chemotherapy, received tepotinib following the detection of a METex14 skipping alteration. Paired pre- and post-treatment biopsies were subjected to GeoMx Digital Spatial Profiling using the Cancer Transcriptome Atlas and immune-related protein panels to evaluate shifts in the immune TME. Tepotinib administration allowed for a successful lobectomy and a pathological downstaging to stage IA1. The TME was transformed from an immunosuppressive to a more permissive state, with upregulation of antigen-presenting and pro-inflammatory immune cells. Moreover, a marked decrease in immune checkpoint molecules, including PD-L1, was noted. Spatial profiling identified discrete immune-enriched clusters, indicating the role of tepotinib in modulating immune cell trafficking and function. Tepotinib appears to remodel the immune TME in a patient with METex14 skipping NSCLC, possibly increasing responsiveness to immunotherapy. Our study supports the integration of genetic profiling into the management of early and locally advanced NSCLC to guide personalized, targeted interventions. These findings underscore the need to further evaluate combinations of MET inhibitors and immunotherapies.
Collapse
Affiliation(s)
- Manon A Simard
- The Healthcare Business of Merck KGaA, 250 Frankfurterstrasse, Darmstadt 64293, Germany
| | | | - Santiago Viteri
- UOMi Cancer Center, Clínica Mi Tres Torres, Barcelona, Spain
| | - Felix Geist
- The Healthcare Business of Merck KGaA, 250 Frankfurterstrasse, Darmstadt 64293, Germany
| | - Nadine Reischmann
- The Healthcare Business of Merck KGaA, 250 Frankfurterstrasse, Darmstadt 64293, Germany
| | - Michael Zühlsdorf
- The Healthcare Business of Merck KGaA, 250 Frankfurterstrasse, Darmstadt 64293, Germany
| | - Niki Karachaliou
- The Healthcare Business of Merck KGaA, 250 Frankfurterstrasse, Darmstadt 64293, Germany.
| |
Collapse
|
2
|
Ak Ç, Sayar Z, Thibault G, Burlingame EA, Kuykendall MJ, Eng J, Chitsazan A, Chin K, Adey AC, Boniface C, Spellman PT, Thomas GV, Kopp RP, Demir E, Chang YH, Stavrinides V, Eksi SE. Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment. iScience 2024; 27:110668. [PMID: 39246442 PMCID: PMC11379676 DOI: 10.1016/j.isci.2024.110668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 07/19/2024] [Accepted: 08/01/2024] [Indexed: 09/10/2024] Open
Abstract
Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.
Collapse
Affiliation(s)
- Çiğdem Ak
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Zeynep Sayar
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Erik A Burlingame
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - M J Kuykendall
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Jennifer Eng
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Alex Chitsazan
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Koei Chin
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Andrew C Adey
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Christopher Boniface
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Paul T Spellman
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - George V Thomas
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, School of Medicine, OHSU, Portland, OR 97239, USA
| | - Ryan P Kopp
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Urology, School of Medicine, Knight Cancer Institute, Portland, OR 97239, USA
| | - Emek Demir
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Division of Oncological Sciences, School of Medicine, OHSU, Portland, OR 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | | | - Sebnem Ece Eksi
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| |
Collapse
|
3
|
Shui L, Maitra A, Yuan Y, Lau K, Kaur H, Li L, Li Z. PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.30.610564. [PMID: 39257799 PMCID: PMC11384012 DOI: 10.1101/2024.08.30.610564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.
Collapse
Affiliation(s)
- Lan Shui
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anirban Maitra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ken Lau
- Biology Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Harsimran Kaur
- Biology Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| |
Collapse
|
4
|
Kumaran G, Carroll L, Muirhead N, Bottomley MJ. How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases? J Invest Dermatol 2024:S0022-202X(24)01926-2. [PMID: 39177547 DOI: 10.1016/j.jid.2024.07.006] [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: 03/01/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 08/24/2024]
Abstract
Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.
Collapse
Affiliation(s)
- Girishkumar Kumaran
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Liam Carroll
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Matthew J Bottomley
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
| |
Collapse
|
5
|
Baas FS, Brusselaers N, Nagtegaal ID, Engstrand L, Boleij A. Navigating beyond associations: Opportunities to establish causal relationships between the gut microbiome and colorectal carcinogenesis. Cell Host Microbe 2024; 32:1235-1247. [PMID: 39146796 DOI: 10.1016/j.chom.2024.07.008] [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: 05/15/2024] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 08/17/2024]
Abstract
The gut microbiota has been recognized as an important determinant in the initiation and progression of colorectal cancer (CRC), with recent studies shining light on the molecular mechanisms that may contribute to the interactions between microbes and the CRC microenvironment. Despite the increasing wealth of associations being established in the field, proving causality remains challenging. Obstacles include the high variability of the microbiome and its context, both across individuals and across time. Additionally, there is a lack of large and representative cohort studies with long-term follow-up and/or appropriate sampling methods for studying the mucosal microbiome. Finally, most studies focus on CRC, whereas interactions between host and bacteria in early events in carcinogenesis remain elusive, reinforced by the heterogeneity of CRC development. Here, we discuss these current most prominent obstacles, the recent developments, and research needs.
Collapse
Affiliation(s)
- Floor S Baas
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nele Brusselaers
- Department of Microbiology, Tumor and Cell Biology, Centre for Translational Microbiome Research, Karolinska Institutet, Karolinska Hospital, Stockholm, Sweden; Global Health Institute, University of Antwerp, Antwerp, Belgium
| | - Iris D Nagtegaal
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Lars Engstrand
- Department of Microbiology, Tumor and Cell Biology, Centre for Translational Microbiome Research, Karolinska Institutet, Karolinska Hospital, Stockholm, Sweden
| | - Annemarie Boleij
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
| |
Collapse
|
6
|
Yang H, Nguyen AQ, Bi D, Buehler MJ, Guo M. Multicell-Fold: geometric learning in folding multicellular life. ARXIV 2024:arXiv:2407.07055v2. [PMID: 39040638 PMCID: PMC11261991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology. However, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. Here we present a geometric deep-learning model that can accurately capture the highly convoluted interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. Using this model, we achieve interpretable 4-D morphological sequence alignment, and predicting cell rearrangements before they occur at single-cell resolution. Furthermore, using neural activation map and ablation studies, we demonstrate cell geometries and cell junction networks together regulate morphogenesis at single-cell precision. This approach offers a pathway toward a unified dynamic atlas for a variety of developmental processes.
Collapse
Affiliation(s)
- Haiqian Yang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Anh Q. Nguyen
- Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Dapeng Bi
- Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Markus J. Buehler
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - Ming Guo
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| |
Collapse
|
7
|
Liao L, Martin PCN, Kim H, Panahandeh S, Won KJ. Data enhancement in the age of spatial biology. Adv Cancer Res 2024; 163:39-70. [PMID: 39271267 DOI: 10.1016/bs.acr.2024.06.008] [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] [Indexed: 09/15/2024]
Abstract
Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.
Collapse
Affiliation(s)
- Linbu Liao
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Denmark; Samuel Oschin Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Patrick C N Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Sanaz Panahandeh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Kyoung Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
| |
Collapse
|
8
|
Doherty-Boyd WS, Donnelly H, Tsimbouri MP, Dalby MJ. Building bones for blood and beyond: the growing field of bone marrow niche model development. Exp Hematol 2024; 135:104232. [PMID: 38729553 DOI: 10.1016/j.exphem.2024.104232] [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: 12/04/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
The bone marrow (BM) niche is a complex microenvironment that provides the signals required for regulation of hematopoietic stem cells (HSCs) and the process of hematopoiesis they are responsible for. Bioengineered models of the BM niche incorporate various elements of the in vivo BM microenvironment, including cellular components, soluble factors, a three-dimensional environment, mechanical stimulation of included cells, and perfusion. Recent advances in the bioengineering field have resulted in a spate of new models that shed light on BM function and are approaching precise imitation of the BM niche. These models promise to improve our understanding of the in vivo microenvironment in health and disease. They also aim to serve as platforms for HSC manipulation or as preclinical models for screening novel therapies for BM-associated disorders and diseases.
Collapse
Affiliation(s)
- W Sebastian Doherty-Boyd
- The Centre for the Cellular Microenvironment (CeMi), University of Glasgow, Glasgow, United Kingdom.
| | - Hannah Donnelly
- School of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Monica P Tsimbouri
- The Centre for the Cellular Microenvironment (CeMi), University of Glasgow, Glasgow, United Kingdom
| | - Matthew J Dalby
- The Centre for the Cellular Microenvironment (CeMi), University of Glasgow, Glasgow, United Kingdom
| |
Collapse
|
9
|
Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [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: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
Collapse
Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
| |
Collapse
|
10
|
Brooks TG, Lahens NF, Mrčela A, Grant GR. Challenges and best practices in omics benchmarking. Nat Rev Genet 2024; 25:326-339. [PMID: 38216661 DOI: 10.1038/s41576-023-00679-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2023] [Indexed: 01/14/2024]
Abstract
Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.
Collapse
Affiliation(s)
- Thomas G Brooks
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas F Lahens
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Antonijo Mrčela
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory R Grant
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
11
|
Jiang W, Caruana DL, Back J, Lee FY. Unique Spatial Transcriptomic Profiling of the Murine Femoral Fracture Callus: A Preliminary Report. Cells 2024; 13:522. [PMID: 38534368 DOI: 10.3390/cells13060522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Fracture callus formation is a dynamic stage of bone activity and repair with precise, spatially localized gene expression. Metastatic breast cancer impairs fracture healing by disrupting bone homeostasis and imparting an altered genomic profile. Previous sequencing techniques such as single-cell RNA and in situ hybridization are limited by missing spatial context and low throughput, respectively. We present a preliminary approach using the Visium CytAssist spatial transcriptomics platform to provide the first spatially intact characterization of genetic expression changes within an orthopedic model of impaired fracture healing. Tissue slides prepared from BALB/c mice with or without MDA-MB-231 metastatic breast cancer cells were used. Both unsupervised clustering and histology-based annotations were performed to identify the hard callus, soft callus, and interzone for differential gene expression between the wild-type and pathological fracture model. The spatial transcriptomics platform successfully localized validated genes of the hard (Dmp1, Sost) and soft callus (Acan, Col2a1). The fibrous interzone was identified as a region of extensive genomic heterogeneity. MDA-MB-231 samples demonstrated downregulation of the critical bone matrix and structural regulators that may explain the weakened bone structure of pathological fractures. Spatial transcriptomics may represent a valuable tool in orthopedic research by providing temporal and spatial context.
Collapse
Affiliation(s)
- Will Jiang
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Dennis L Caruana
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Jungho Back
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Francis Y Lee
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| |
Collapse
|
12
|
de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
Collapse
Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
| |
Collapse
|
13
|
Pascual-Reguant A, Kroh S, Hauser AE. Tissue niches and immunopathology through the lens of spatial tissue profiling techniques. Eur J Immunol 2024; 54:e2350484. [PMID: 37985207 DOI: 10.1002/eji.202350484] [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: 05/31/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/22/2023]
Abstract
Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.
Collapse
Affiliation(s)
- Anna Pascual-Reguant
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
- Spatial Genomics, Centre Nacional d'Anàlisi Genòmica, Barcelona, 08028, Spain
| | - Sandy Kroh
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| | - Anja E Hauser
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| |
Collapse
|
14
|
Femel J, Hill C, Illa Bochaca I, Booth JL, Asnaashari TG, Steele MM, Moshiri AS, Do H, Zhong J, Osman I, Leachman SA, Tsujikawa T, White KP, Chang YH, Lund AW. Quantitative multiplex immunohistochemistry reveals inter-patient lymphovascular and immune heterogeneity in primary cutaneous melanoma. Front Immunol 2024; 15:1328602. [PMID: 38361951 PMCID: PMC10867179 DOI: 10.3389/fimmu.2024.1328602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
Introduction Quantitative, multiplexed imaging is revealing complex spatial relationships between phenotypically diverse tumor infiltrating leukocyte populations and their prognostic implications. The underlying mechanisms and tissue structures that determine leukocyte distribution within and around tumor nests, however, remain poorly understood. While presumed players in metastatic dissemination, new preclinical data demonstrates that blood and lymphatic vessels (lymphovasculature) also dictate leukocyte trafficking within tumor microenvironments and thereby impact anti-tumor immunity. Here we interrogate these relationships in primary human cutaneous melanoma. Methods We established a quantitative, multiplexed imaging platform to simultaneously detect immune infiltrates and tumor-associated vessels in formalin-fixed paraffin embedded patient samples. We performed a discovery, retrospective analysis of 28 treatment-naïve, primary cutaneous melanomas. Results Here we find that the lymphvasculature and immune infiltrate is heterogenous across patients in treatment naïve, primary melanoma. We categorized five lymphovascular subtypes that differ by functionality and morphology and mapped their localization in and around primary tumors. Interestingly, the localization of specific vessel subtypes, but not overall vessel density, significantly associated with the presence of lymphoid aggregates, regional progression, and intratumoral T cell infiltrates. Discussion We describe a quantitative platform to enable simultaneous lymphovascular and immune infiltrate analysis and map their spatial relationships in primary melanoma. Our data indicate that tumor-associated vessels exist in different states and that their localization may determine potential for metastasis or immune infiltration. This platform will support future efforts to map tumor-associated lymphovascular evolution across stage, assess its prognostic value, and stratify patients for adjuvant therapy.
Collapse
Affiliation(s)
- Julia Femel
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
| | - Cameron Hill
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Irineu Illa Bochaca
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Jamie L. Booth
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
| | - Tina G. Asnaashari
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
| | - Maria M. Steele
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Ata S. Moshiri
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Hyungrok Do
- Department of Population Health, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Judy Zhong
- Department of Population Health, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
| | - Iman Osman
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
| | - Sancy A. Leachman
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Takahiro Tsujikawa
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
- Department of Otolaryngology-Head and Neck Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kevin P. White
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
| | - Young H. Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Amanda W. Lund
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
- Department of Pathology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| |
Collapse
|
15
|
Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
Collapse
Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
| |
Collapse
|
16
|
Nirmal AJ, Yapp C, Santagata S, Sorger PK. Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.15.567196. [PMID: 38014110 PMCID: PMC10680730 DOI: 10.1101/2023.11.15.567196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.
Collapse
Affiliation(s)
- Ajit J. Nirmal
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clarence Yapp
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sandro Santagata
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K. Sorger
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| |
Collapse
|
17
|
El Marrahi A, Lipreri F, Kang Z, Gsell L, Eroglu A, Alber D, Hausser J. NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology. Nat Commun 2023; 14:7182. [PMID: 37935691 PMCID: PMC10630431 DOI: 10.1038/s41467-023-42878-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] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.
Collapse
Affiliation(s)
- Anissa El Marrahi
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Fabio Lipreri
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Ziqi Kang
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Louise Gsell
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Alper Eroglu
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - David Alber
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden
- SciLifeLab; Solna, Stockholm, 171 65, Sweden
| | - Jean Hausser
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 171 77, Sweden.
- SciLifeLab; Solna, Stockholm, 171 65, Sweden.
| |
Collapse
|
18
|
Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
Collapse
Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| |
Collapse
|
19
|
Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
Collapse
Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| |
Collapse
|
20
|
Park HE, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
Collapse
Affiliation(s)
- Han-Eol Park
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
- School of Biological Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Rosalind H Lee
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Christian P Macks
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Chung Whan Lee
- Department of Chemistry, Gachon University, Seongnam, Gyeonggi-do, 13120, Republic of Korea
| | - Junho K Hur
- Department of Genetics, College of Medicine, Hanyang University, Seoul, 04763, Republic of Korea
| | - Chang Ho Sohn
- Center for Nanomedicine, Institute for Basic Science, Yonsei University, Seoul, 03722, Republic of Korea
- Graduate Program in Nanobiomedical Engineering, Advanced Science Institute, Yonsei University, Seoul, 03722, Republic of Korea
| |
Collapse
|
21
|
Designing spatial transcriptomic experiments. Nat Methods 2023; 20:355-356. [PMID: 36864198 DOI: 10.1038/s41592-023-01801-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
|