Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

Interface-guided phenotyping of coding variants in the transcription factor RUNX1 with SEUSS

Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions.

Global decoupling of cell differentiation from cell division in early embryo development

As tissues develop, cells divide and differentiate concurrently. Conflicting evidence shows that cell division is either dispensable or required for formation of cell types. To determine the role of cell division in differentiation, we arrested the cell cycle in zebrafish embryos using two independent approaches and profiled them at single-cell resolution. We show that cell division is dispensable for differentiation of all embryonic tissues during initial cell type differentiation from early gastrulation to the end of segmentation. In the absence of cell division, differentiation slows down in some cell types, and cells exhibit global stress responses. While differentiation is robust to blocking cell division, the proportions of cells across cell states are not. This work simplifies our understanding of the role of cell division in development and showcases the utility of combining embryo-wide perturbations with single-cell RNA sequencing to uncover the role of common biological processes across multiple tissues.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Spatial cellular architecture predicts prognosis in glioblastoma

Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes.

Mitophagy Promotes Resistance to BH3 Mimetics in Acute Myeloid Leukemia

BH3 mimetics are used as an efficient strategy to induce cell death in several blood malignancies, including acute myeloid leukemia (AML). Venetoclax, a potent BCL-2 antagonist, is used clinically in combination with hypomethylating agents for the treatment of AML. Moreover, MCL1 or dual BCL-2/BCL-xL antagonists are under investigation. Yet, resistance to single or combinatorial BH3-mimetic therapies eventually ensues. Integration of multiple genome-wide CRISPR/Cas9 screens revealed that loss of mitophagy modulators sensitizes AML cells to various BH3 mimetics targeting different BCL-2 family members. One such regulator is MFN2, whose protein levels positively correlate with drug resistance in patients with AML. MFN2 overexpression is sufficient to drive resistance to BH3 mimetics in AML. Insensitivity to BH3 mimetics is accompanied by enhanced mitochondria-endoplasmic reticulum interactions and augmented mitophagy flux, which acts as a prosurvival mechanism to eliminate mitochondrial damage. Genetic or pharmacologic MFN2 targeting synergizes with BH3 mimetics by impairing mitochondrial clearance and enhancing apoptosis in AML.

SIGNIFICANCE

AML remains one of the most difficult-to-treat blood cancers. BH3 mimetics represent a promising therapeutic approach to eliminate AML blasts by activating the apoptotic pathway. Enhanced mitochondrial clearance drives resistance to BH3 mimetics and predicts poor prognosis. Reverting excessive mitophagy can halt BH3-mimetic resistance in AML. This article is highlighted in the In This Issue feature, p. 1501.

Hypergraph factorization for multi-tissue gene expression imputation

Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

CD168+ macrophages promote hepatocellular carcinoma tumor stemness and progression through TOP2A/β-catenin/YAP1 axis

Liver cancer stem-like cells (LCSCs) are the main cause of heterogeneity and poor prognosis in hepatocellular carcinoma (HCC). In this study, we aimed to explore the origin of LCSCs and the role of the TOP2A/β-catenin/YAP1 axis in tumor stemness and progression. Using single-cell RNA-seq analysis, we identified TOP2A+CENPF+ LCSCs, which were mainly regulated by CD168+ M2-like macrophages. Furthermore, spatial location analysis and fluorescent staining confirmed that LCSCs were enriched at tumor margins, constituting the spatial heterogeneity of HCC. Mechanistically, TOP2A competitively binds to β-catenin, leading to disassociation of β-catenin from YAP1, promoting HCC stemness and overgrowth. Our study provides valuable insights into the spatial transcriptome heterogeneity of the HCC microenvironment and the critical role of TOP2A/β-catenin/YAP1 axis in HCC stemness and progression.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

Transcriptional space-time mapping identifies concerted immune and stromal cell patterns and gene programs in wound healing and cancer

Tissue repair responses in metazoans are highly coordinated by different cell types over space and time. However, comprehensive single-cell-based characterization covering this coordination is lacking. Here, we captured transcriptional states of single cells over space and time during skin wound closure, revealing choreographed gene-expression profiles. We identified shared space-time patterns of cellular and gene program enrichment, which we call multicellular "movements" spanning multiple cell types. We validated some of the discovered space-time movements using large-volume imaging of cleared wounds and demonstrated the value of this analysis to predict "sender" and "receiver" gene programs in macrophages and fibroblasts. Finally, we tested the hypothesis that tumors are like "wounds that never heal" and found conserved wound healing movements in mouse melanoma and colorectal tumor models, as well as human tumor samples, revealing fundamental multicellular units of tissue biology for integrative studies.

Intratumoral dendritic cell-CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma

Despite no apparent defects in T cell priming and recruitment to tumors, a large subset of T cell rich tumors fail to respond to immune checkpoint blockade (ICB). We leveraged a neoadjuvant anti-PD-1 trial in patients with hepatocellular carcinoma (HCC), as well as additional samples collected from patients treated off-label, to explore correlates of response to ICB within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells ("CXCL13+ TH") and Granzyme K+ PD-1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells dominated in nonresponders. CD4+ and CD8+ T cell clones that expanded post-treatment were found in pretreatment biopsies. Notably, PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones mainly with effector-like cells in responders or terminally exhausted cells in nonresponders, suggesting that local CD8+ T cell differentiation occurs upon ICB. We found that these Progenitor CD8+ T cells interact with CXCL13+ TH within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or "mregDC". These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ TH control the differentiation of tumor-specific Progenitor exhasuted CD8+ T cells following ICB.

Coral-algal endosymbiosis characterized using RNAi and single-cell RNA-seq

Corals form an endosymbiotic relationship with the dinoflagellate algae Symbiodiniaceae, but ocean warming can trigger algal loss, coral bleaching and death, and the degradation of ecosystems. Mitigation of coral death requires a mechanistic understanding of coral-algal endosymbiosis. Here we report an RNA interference (RNAi) method and its application to study genes involved in early steps of endosymbiosis in the soft coral Xenia sp. We show that a host endosymbiotic cell marker called LePin (lectin and kazal protease inhibitor domains) is a secreted Xenia lectin that binds to algae to initiate phagocytosis of the algae and coral immune response modulation. The evolutionary conservation of domains in LePin among marine anthozoans performing endosymbiosis suggests a general role in coral-algal recognition. Our work sheds light on the phagocytic machinery and posits a mechanism for symbiosome formation, helping in efforts to understand and preserve coral-algal relationships in the face of climate change.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Single-Cell Transcriptomics Reveals Evidence of Endothelial Dysfunction in the Brains of COVID-19 Patients with Implications for Glioblastoma Progression

BACKGROUND

Endothelial dysfunction is implicated in various inflammatory diseases such as ischemic stroke, heart attack, organ failure, and COVID-19. Recent studies have shown that endothelial dysfunction in the brain is attributed to excessive inflammatory responses caused by the SARS-CoV-2 infection, leading to increased permeability of the blood-brain barrier and consequently neurological damage. Here, we aim to examine the single-cell transcriptomic landscape of endothelial dysfunction in COVID-19 and its implications for glioblastoma (GBM) progression.

METHODS

Single-cell transcriptome data GSE131928 and GSE159812 were obtained from the gene expression omnibus (GEO) to analyze the expression profiles of key players in innate immunity and inflammation between brain endothelial dysfunction caused by COVID-19 and GBM progression.

RESULTS

Single-cell transcriptomic analysis of the brain of COVID-19 patients revealed that endothelial cells had undergone significant transcriptomic changes, with several genes involved in immune responses and inflammation upregulated. Moreover, transcription factors were observed to modulate this inflammation, including interferon-regulated genes.

CONCLUSIONS

The results indicate a significant overlap between COVID-19 and GBM in the context of endothelial dysfunction, suggesting that there may be an endothelial dysfunction link connecting severe SARS-CoV-2 infection in the brain to GBM progression.

Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed that neutrophils and emergency granulopoiesis drove a maladaptive response during sepsis. We generated a whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune response that identified populations of immunosuppressive mature and immature neutrophils. In co-culture, CD66b⁺ sepsis neutrophils inhibited proliferation and activation of CD4⁺ T cells. Single-cell multiomic mapping of circulating hematopoietic stem and progenitor cells (HSPCs) (29,366 cells, n = 27) indicated altered granulopoiesis in patients with sepsis. These features were enriched in a patient subset with poor outcome and a specific sepsis response signature that displayed higher frequencies of IL1R2⁺ immature neutrophils, epigenetic and transcriptomic signatures of emergency granulopoiesis in HSPCs and STAT3-mediated gene regulation across different infectious etiologies and syndromes. Our findings offer potential therapeutic targets and opportunities for stratified medicine in severe infection.

Graded BMP signaling within intestinal crypt architecture directs self-organization of the Wnt-secreting stem cell niche

Signals from the surrounding niche drive proliferation and suppress differentiation of intestinal stem cells (ISCs) at the bottom of intestinal crypts. Among sub-epithelial support cells, deep sub-cryptal CD81+ PDGFRAlo trophocytes capably sustain ISC functions ex vivo. Here, we show that mRNA and chromatin profiles of abundant CD81- PDGFRAlo mouse stromal cells resemble those of trophocytes and that both populations provide crucial canonical Wnt ligands. Mesenchymal expression of key ISC-supportive factors extends along a spatial and molecular continuum from trophocytes into peri-cryptal CD81- CD55hi cells, which mimic trophocyte activity in organoid co-cultures. Graded expression of essential niche factors is not cell-autonomous but dictated by the distance from bone morphogenetic protein (BMP)-secreting PDGFRAhi myofibroblast aggregates. BMP signaling inhibits ISC-trophic genes in PDGFRAlo cells near high crypt tiers; that suppression is relieved in stromal cells near and below the crypt base, including trophocytes. Cell distances thus underlie a self-organized and polar ISC niche.

Differentiation trajectories of the Hydra nervous system reveal transcriptional regulators of neuronal fate

The small freshwater cnidarian polyp Hydra vulgaris uses adult stem cells (interstitial stem cells) to continually replace neurons throughout its life. This feature, combined with the ability to image the entire nervous system (Badhiwala et al., 2021; Dupre & Yuste, 2017) and availability of gene knockdown techniques (Juliano, Reich, et al., 2014; Lohmann et al., 1999; Vogg et al., 2022), makes Hydra a tractable model for studying nervous system development and regeneration at the whole-organism level. In this study, we use single-cell RNA sequencing and trajectory inference to provide a comprehensive molecular description of the adult nervous system. This includes the most detailed transcriptional characterization of the adult Hydra nervous system to date. We identified eleven unique neuron subtypes together with the transcriptional changes that occur as the interstitial stem cells differentiate into each subtype. Towards the goal of building gene regulatory networks to describe Hydra neuron differentiation, we identified 48 transcription factors expressed specifically in the Hydra nervous system, including many that are conserved regulators of neurogenesis in bilaterians. We also performed ATAC-seq on sorted neurons to uncover previously unidentified putative regulatory regions near neuron-specific genes. Finally, we provide evidence to support the existence of transdifferentiation between mature neuron subtypes and we identify previously unknown transition states in these pathways. All together, we provide a comprehensive transcriptional description of an entire adult nervous system, including differentiation and transdifferentiation pathways, which provides a significant advance towards understanding mechanisms that underlie nervous system regeneration.

Remodeling of colon plasma cell repertoire within ulcerative colitis patients

Plasma cells (PCs) constitute a significant fraction of colonic mucosal cells and contribute to inflammatory infiltrates in ulcerative colitis (UC). While gut PCs secrete bacteria-targeting IgA antibodies, their role in UC pathogenesis is unknown. We performed single-cell V(D)J- and RNA-seq on sorted B cells from the colon of healthy individuals and patients with UC. A large fraction of B cell clones is shared between different colon regions, but inflammation in UC broadly disrupts this landscape, causing transcriptomic changes characterized by an increase in the unfolded protein response (UPR) and antigen presentation genes, clonal expansion, and isotype skewing from IgA1 and IgA2 to IgG1. We also directly expressed and assessed the specificity of 152 mAbs from expanded PC clones. These mAbs show low polyreactivity and autoreactivity and instead target both shared bacterial antigens and specific bacterial strains. Altogether, our results characterize the microbiome-specific colon PC response and how its disruption might contribute to inflammation in UC.

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Esophageal Squamous Cell Carcinoma

Lymph node metastasis, the leading cause of mortality in esophageal squamous carcinoma (ESCC) with a highly complex tumor microenvironment, remains underexplored. Here, the transcriptomes of 85 263 single cells are analyzed from four ESCC patients with lymph node metastases. Strikingly, it is observed that the metastatic microenvironment undergoes the emergence or expansion of interferon induced IFIT3+ T, B cells, and immunosuppressive cells such as APOC1+ APOE+ macrophages and myofibroblasts with highly expression of immunoglobulin genes (IGKC) and extracellular matrix component and matrix metallopeptidase genes. A poor-prognostic epithelial-immune dual expression program regulating immune effector processes, whose activity is significantly enhanced in metastatic malignant epithelial cells and enriched in CD74+ CXCR4+ and major histocompatibility complex (MHC) class II genes upregulated malignant epithelia cells is discovered. Comparing with primary tumor, differential intercellular communications of metastatic ESCC microenvironment are revealed and furtherly validated via multiplexed immunofluorescence and immunohistochemistry staining, which mainly rely on the crosstalk of APOC1+ APOE+ macrophages with tumor and stromal cell. The data highlight potential molecular mechanisms that shape the lymph-node metastatic microenvironment and may inform drug discovery and the development of new strategies to target these prometastatic nontumor components for inhibiting tumor growth and overcoming metastasis to improve clinical outcomes.

Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Epstein-Barr virus DNA seropositivity links distinct tumoral heterogeneity and immune landscape in nasopharyngeal carcinoma

Background

Epstein-Barr virus (EBV) DNA seronegative (Sero-) and seropositive (Sero+) nasopharyngeal carcinoma (NPC) are distinctly different disease subtypes. Patients with higher baseline EBV DNA titers seem to benefit less from anti-PD1 immunotherapy, but underlying mechanisms remain unclear. Tumor microenvironment (TME) characteristics could be the important factor affecting the efficacy of immunotherapy. Here, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from cellular compositional and functional perspectives at single-cell resolution.

Method

We performed single-cell RNA sequencing analyses of 28,423 cells from ten NPC samples and one non-tumor nasopharyngeal tissue. The markers, function, and dynamics of related cells were analyzed.

Results

We found that tumor cells from EBV DNA Sero+ samples exhibit low-differentiation potential, stronger stemness signature, and upregulated signaling pathways associated with cancer hallmarks than that of EBV DNA Sero- samples. Transcriptional heterogeneity and dynamics in T cells were associated with EBV DNA seropositivity status, indicating different immunoinhibitory mechanisms employed by malignant cells depending on EBV DNA seropositivity status. The low expression of classical immune checkpoints, early-triggered cytotoxic T-lymphocyte response, global activation of IFN-mediated signatures, and enhanced cell-cell interplays cooperatively tend to form a specific immune context in EBV DNA Sero+ NPC.

Conclusions

Collectively, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from single-cell perspective. Our study provides insights into the altered tumor microenvironment of NPC associated with EBV DNA seropositivity, which will help direct the development of rational immunotherapy strategies.

Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing

Here we introduce a mostly natural sequencing-by-synthesis (mnSBS) method for single-cell RNA sequencing (scRNA-seq), adapted to the Ultima genomics platform, and systematically benchmark it against current scRNA-seq technology. mnSBS uses mostly natural, unmodified nucleotides and only a low fraction of fluorescently labeled nucleotides, which allows for high polymerase processivity and lower costs. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5' and 3' scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Benchmarking shows that results from mnSBS-based scRNA-seq are very similar to those using Illumina sequencing, with minor differences in results related to the position of reads relative to annotated gene boundaries, owing to single-end reads of Ultima being closer to gene ends than reads from Illumina. The method is thus compatible with state-of-the-art scRNA-seq libraries independent of the sequencing technology. We expect mnSBS to be of particular utility for cost-effective large-scale scRNA-seq projects.

A chromosome-scale epigenetic map of the Hydra genome reveals conserved regulators of cell state

The epithelial and interstitial stem cells of the freshwater polyp Hydra are the best-characterized stem cell systems in any cnidarian, providing valuable insight into cell type evolution and the origin of stemness in animals. However, little is known about the transcriptional regulatory mechanisms that determine how these stem cells are maintained and how they give rise to their diverse differentiated progeny. To address such questions, a thorough understanding of transcriptional regulation in Hydra is needed. To this end, we generated extensive new resources for characterizing transcriptional regulation in Hydra, including new genome assemblies for Hydra oligactis and the AEP strain of Hydra vulgaris, an updated whole-animal single-cell RNA-seq atlas, and genome-wide maps of chromatin interactions, chromatin accessibility, sequence conservation, and histone modifications. These data revealed the existence of large kilobase-scale chromatin interaction domains in the Hydra genome that contain transcriptionally coregulated genes. We also uncovered the transcriptomic profiles of two previously molecularly uncharacterized cell types: isorhiza-type nematocytes and somatic gonad ectoderm. Finally, we identified novel candidate regulators of cell type-specific transcription, several of which have likely been conserved at least since the divergence of Hydra and the jellyfish Clytia hemisphaerica more than 400 million years ago.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Compressed phenotypic screens for complex multicellular models and high-content assays

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq

Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S3 (S3: Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S3, along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data.

An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematopoietic malignancy with poor prognosis and limited treatment options. Here we provide a comprehensive census of the bone marrow immune microenvironment in adult and pediatric patients with AML. We characterize unique inflammation signatures in a subset of AML patients, associated with inferior outcomes. We identify atypical B cells, a dysfunctional B-cell subtype enriched in patients with high-inflammation AML, as well as an increase in CD8+GZMK+ and regulatory T cells, accompanied by a reduction in T-cell clonal expansion. We derive an inflammation-associated gene score (iScore) that associates with poor survival outcomes in patients with AML. Addition of the iScore refines current risk stratifications for patients with AML and may enable identification of patients in need of more aggressive treatment. This work provides a framework for classifying patients with AML based on their immune microenvironment and a rationale for consideration of the inflammatory state in clinical settings.

Learning cell identity in immunology, neuroscience, and cancer

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Single-cell RNA-seq methods to interrogate virus-host interactions

The twenty-first century has seen the emergence of many epidemic and pandemic viruses, with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate intracellular parasites, viruses rely on host cells to replicate and produce progeny, resulting in complex virus and host dynamics during an infection. Single-cell RNA sequencing (scRNA-seq), by enabling broad and simultaneous profiling of both host and virus transcripts, represents a powerful technology to unravel the delicate balance between host and virus. In this review, we summarize technological and methodological advances in scRNA-seq and their applications to antiviral immunity. We highlight key scRNA-seq applications that have enabled the understanding of viral genomic and host response heterogeneity, differential responses of infected versus bystander cells, and intercellular communication networks. We expect further development of scRNA-seq technologies and analytical methods, combined with measurements of additional multi-omic modalities and increased availability of publicly accessible scRNA-seq datasets, to enable a better understanding of viral pathogenesis and enhance the development of antiviral therapeutics strategies.

Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment

BACKGROUND

The tumor microenvironment (TME) has been shown to strongly influence treatment outcome for cancer patients in various indications and to influence the overall survival. However, the cells forming the TME in gastric cancer have not been extensively characterized.

RESULTS

We combine bulk and single-cell RNA sequencing from tumors and matched normal tissue of 24 treatment-naïve GC patients to better understand which cell types and transcriptional programs are associated with malignant transformation of the stomach. Clustering 96,623 cells of non-epithelial origin reveals 81 well-defined TME cell types. We find that activated fibroblasts and endothelial cells are most prominently overrepresented in tumors. Intercellular network reconstruction and survival analysis of an independent cohort imply the importance of these cell types together with immunosuppressive myeloid cell subsets and regulatory T cells in establishing an immunosuppressive microenvironment that correlates with worsened prognosis and lack of response in anti-PD1-treated patients. In contrast, we find a subset of IFNγ activated T cells and HLA-II expressing macrophages that are linked to treatment response and increased overall survival.

CONCLUSIONS

Our gastric cancer single-cell TME compendium together with the matched bulk transcriptome data provides a unique resource for the identification of new potential biomarkers for patient stratification. This study helps further to elucidate the mechanism of gastric cancer and provides insights for therapy.

A map of signaling responses in the human airway epithelium

Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of all epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal-cell and secretory-cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses.

Correlated gene modules uncovered by high-precision single-cell transcriptomics

Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.

A Poisson reduced-rank regression model for association mapping in sequencing data

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions.

RESULTS

We aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses.

CONCLUSION

We show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states.

Cellular interactions in the pituitary stem cell niche

Stem cells in the anterior pituitary gland can give rise to all resident endocrine cells and are integral components for the appropriate development and subsequent maintenance of the organ. Located in discreet niches within the gland, stem cells are involved in bi-directional signalling with their surrounding neighbours, interactions which underpin pituitary gland homeostasis and response to organ challenge or physiological demand. In this review we highlight core signalling pathways that steer pituitary progenitors towards specific endocrine fate decisions throughout development. We further elaborate on those which are conserved in the stem cell niche postnatally, including WNT, YAP/TAZ and Notch signalling. Furthermore, we have collated a directory of single cell RNA sequencing studies carried out on pituitaries across multiple organisms, which have the potential to provide a vast database to study stem cell niche components in an unbiased manner. Reviewing published data, we highlight that stem cells are one of the main signalling hubs within the anterior pituitary. In future, coupling single cell sequencing approaches with genetic manipulation tools in vivo, will enable elucidation of how previously understudied signalling pathways function within the anterior pituitary stem cell niche.

K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas

Canonical (H3.1/H3.2) and noncanonical (H3.3) histone 3 K27M-mutant gliomas have unique spatiotemporal distributions, partner alterations and molecular profiles. The contribution of the cell of origin to these differences has been challenging to uncouple from the oncogenic reprogramming induced by the mutation. Here, we perform an integrated analysis of 116 tumors, including single-cell transcriptome and chromatin accessibility, 3D chromatin architecture and epigenomic profiles, and show that K27M-mutant gliomas faithfully maintain chromatin configuration at developmental genes consistent with anatomically distinct oligodendrocyte precursor cells (OPCs). H3.3K27M thalamic gliomas map to prosomere 2-derived lineages. In turn, H3.1K27M ACVR1-mutant pontine gliomas uniformly mirror early ventral NKX6-1⁺/SHH-dependent brainstem OPCs, whereas H3.3K27M gliomas frequently resemble dorsal PAX3⁺/BMP-dependent progenitors. Our data suggest a context-specific vulnerability in H3.1K27M-mutant SHH-dependent ventral OPCs, which rely on acquisition of ACVR1 mutations to drive aberrant BMP signaling required for oncogenesis. The unifying action of K27M mutations is to restrict H3K27me3 at PRC2 landing sites, whereas other epigenetic changes are mainly contingent on the cell of origin chromatin state and cycling rate.

Impacts of neoadjuvant chemoradiotherapy on the immune landscape of esophageal squamous cell carcinoma

BACKGROUND

Neoadjuvant chemoradiotherapy (neoCRT) followed by surgery is the most common approach for locally advanced resectable esophageal squamous cell carcinoma (ESCC) patients. How neoCRT impacts ESCC tumor immune microenvironment (TIME) has not been fully understood.

METHODS

Single-cell RNA sequencing (scRNA-seq) was conducted to examine the neoCRT-driven cellular and molecular dynamics in 8 pre- and 7 post-neoCRT ESCC samples from 8 male patients.

FINDINGS

scRNA-seq data of about 112,000 cells were obtained. Expression programs of cell cycle, epithelium development, immune response, and extracellular structure in pre-treatment tumor cells were related to neoCRT response. Spearman correlation between CD8⁺ T cells' cytotoxicity and expression of checkpoint molecules was prominent in pre-neoCRT intermediate activated/exhausted CD8⁺ T cells. NeoCRT increased CD8⁺ T cells' infiltration but promoted their exhaustion in both major and minor responders. NeoCRT promoted differentiation of Th but demoted that of Treg cells in major responders. Maturation of cDC1s and expression of M2 macrophage markers increased while the number of cDC2s decreased after neoCRT. Higher activities of immune-related pathways in pre-neoCRT CD8⁺ T cells and macrophages, as well as a pronounced decrease of them after neoCRT, correlated with better neoCRT response. Interactions between intermediate activated/exhausted CD8⁺ T and macrophages, cDC1s, and LAMP3⁺ cDCs decreased after neoCRT.

INTERPRETATION

Our comprehensive picture of the neoCRT-related immune changes provides deeper insights into immunological mechanisms associated with ESCC response to neoCRT, which may aid in future development of immune-strategies for improving ESCC treatment.

FUNDING

This work was supported by the National Natural Science Foundation of China (82072607).

How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

Single-cell transcriptomic analysis of the tumor ecosystem of adenoid cystic carcinoma

Adenoid cystic carcinoma (ACC) is a malignant tumor that originates from exocrine gland epithelial cells. We profiled the transcriptomes of 49,948 cells from paracarcinoma and carcinoma tissues of three patients using single-cell RNA sequencing. Three main types of the epithelial cells were identified into myoepithelial-like cells, intercalated duct-like cells, and duct-like cells by marker genes. And part of intercalated duct-like cells with special copy number variations which altered with MYB family gene and EN1 transcriptomes were identified as premalignant cells. Developmental pseudo-time analysis showed that the premalignant cells eventually transformed into malignant cells. Furthermore, MYB and MYBL1 were found to belong to two different gene modules and were expressed in a mutually exclusive manner. The two gene modules drove ACC progression into different directions. Our findings provide novel evidence to explain the high recurrence rate of ACC and its characteristic biological behavior.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what others might exist that scientists have not thought to look for. Furthermore, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images. Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low- to mid-level visual features, such as color, shape, or texture, failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

Decoding the colorectal cancer ecosystem emphasizes the cooperative role of cancer cells, TAMs and CAFsin tumor progression

Background

Single-cell transcription data provided unprecedented molecular information, enabling us to directly encode the ecosystem of colorectal cancer (CRC). Characterization of the diversity of epithelial cells and how they cooperate with tumor microenvironment cells (TME) to endow CRC with aggressive characteristics at single-cell resolution is critical for the understanding of tumor progression mechanism.

Methods

In this study, we comprehensively analyzed the single-cell transcription data, bulk-RNA sequencing data and pathological tissue data. In detail, cellular heterogeneity of TME and epithelial cells were analyzed by unsupervised classification and consensus nonnegative matrix factorization analysis, respectively. Functional status of epithelial clusters was annotated by CancerSEA and its crosstalk with TME cells was investigated using CellPhoneDB and correlation analysis. Findings from single-cell transcription data were further validated in bulk-RNA sequencing data and pathological tissue data.

Results

A distinct cellular composition was observed between tumor and normal tissues, and tumors exhibited immunosuppressive phenotypes. Regarding epithelial cells, we identified one highly invasiveQuery cluster, C4, that correlated closely with tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs). Further analysis emphasized the TAMs subclass TAM1 and CAFs subclass S5 are closely related with C4.

Conclusions

In summary, our study elaborates on the cellular heterogeneity of CRC, revealing that TAMs and CAFs were critical for crosstalk network epithelial cells and TME cells. This in-depth understanding of cancer cell-TME network provided theoretical basis for the development of new drugs targeting this sophisticated network in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03661-8.

RNA-Responsive gRNAs for Controlling CRISPR Activity: Current Advances, Future Directions, and Potential Applications

CRISPR-Cas9 has emerged as a major genome manipulation tool. As Cas9 can cause off-target effects, several methods for controlling the expression of CRISPR systems were developed. Recent studies have shown that CRISPR activity could be controlled by sensing expression levels of endogenous transcripts. This is particularly interesting, as endogenous RNAs could harbor important information about the cell type, disease state, and environmental challenges cells are facing. Single-guide RNA (sgRNA) engineering played a major role in the development of RNA-responsive CRISPR systems. Following further optimizations, RNA-responsive sgRNAs could enable the development of novel therapeutic and research applications. This review introduces engineering strategies that could be employed to modify Streptococcus pyogenes sgRNAs with a focus on recent advances made toward the development of RNA-responsive sgRNAs. Future directions and potential applications of these technologies are also discussed.

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Mapping hormone-regulated cell-cell interaction networks in the human breast at single-cell resolution

The rise and fall of estrogen and progesterone across menstrual cycles and during pregnancy regulates breast development and modifies cancer risk. How these hormones impact each cell type in the breast remains poorly understood because they act indirectly through paracrine networks. Using single-cell analysis of premenopausal breast tissue, we reveal a network of coordinated transcriptional programs representing the tissue-level response to changing hormone levels. Our computational approach, DECIPHER-seq, leverages person-to-person variability in breast composition and cell state to uncover programs that co-vary across individuals. We use differences in cell-type proportions to infer a subset of programs that arise from direct cell-cell interactions regulated by hormones. Further, we demonstrate that prior pregnancy and obesity modify hormone responsiveness through distinct mechanisms: obesity reduces the proportion of hormone-responsive cells, whereas pregnancy dampens the direct response of these cells to hormones. Together, these results provide a comprehensive map of the cycling human breast.

Club cells employ regeneration mechanisms during lung tumorigenesis

The high plasticity of lung epithelial cells, has for many years, confounded the correct identification of the cell-of-origin of lung adenocarcinoma (LUAD), one of the deadliest malignancies worldwide. Here, we employ lineage-tracing mouse models to investigate the cell of origin of Eml4-Alk LUAD, and show that Club and Alveolar type 2 (AT2) cells give rise to tumours. We focus on Club cell originated tumours and find that Club cells experience an epigenetic switch by which they lose their lineage fidelity and gain an AT2-like phenotype after oncogenic transformation. Single-cell transcriptomic analyses identified two trajectories of Club cell evolution which are similar to the ones used during lung regeneration, suggesting that lung epithelial cells leverage on their plasticity and intrinsic regeneration mechanisms to give rise to a tumour. Together, this study highlights the role of Club cells in LUAD initiation, identifies the mechanism of Club cell lineage infidelity, confirms the presence of these features in human tumours, and unveils key mechanisms conferring LUAD heterogeneity.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

Single-cell transcriptional regulation and genetic evolution of neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer, with a 10% five-year survival rate. However, little is known about its origin and the mechanisms governing its emergence. Our study characterized ADPC and NEPC in prostate tumors from 7 patients using scRNA-seq. First, we identified two NEPC gene expression signatures representing different phases of trans-differentiation. New marker genes we identified may be used for clinical diagnosis. Second, integrative analyses combining expression and subclonal architecture revealed different paths by which NEPC diverges from the original ADPC, either directly from treatment-naïve tumor cells or from specific intermediate states of treatment-resistance. Third, we inferred a hierarchical transcription factor (TF) network underlying the progression, which involves constitutive regulation by ASCL1, FOXA2, and selective regulation by NKX2-2, POU3F2, and SOX2. Together, these results defined the complex expression profiles and advanced our understanding of the genetic and transcriptomic mechanisms leading to NEPC differentiation.

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Single-cell RNA sequencing revealed two distinct transcriptional programs of NEPC

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Cell-level clonal evolution analysis extended the divergent model of ADPC to NEPC

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Screening of NEPC-specific transcription factors through network-based approaches