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High-resolution maps of embryonic development suggest that acquisition of cell identity is not limited to canonical germ layers but proceeds via alternative routes. Despite evidence that visceral organs are formed via embryonic and extra-embryonic trajectories, the production of organ-specific cell types in vitro focuses on the embryonic one. Here we resolve these differentiation routes using massively parallel single-cell RNA sequencing to generate datasets from FOXA2

Multiple studies identified metabolic changes within the tumor and its microenvironment during carcinogenesis. Yet, the mechanisms by which tumors affect the host metabolism are unclear. We find that systemic inflammation induced by the cancer leads to liver infiltration of myeloid cells at early extrahepatic carcinogenesis. The infiltrating immune cells via IL-6-pSTAT3 immune-hepatocyte crosstalk cause the depletion of a master metabolic regulator, HNF4a, consequently leading to systemic metabolic changes that promote breast and pancreatic cancer proliferation and a worse outcome. Preserving HNF4 levels maintains liver metabolism and restricts carcinogenesis. Standard liver biochemical tests can identify early metabolic changes and predict patients' outcomes and weight loss. Thus, the tumor induces early metabolic changes in its macro-environment with diagnostic and potentially therapeutic implications for the host.

Development of multicellular organisms is orchestrated by persistent cell–cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell–cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell–cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate Lhx5 and Nkx2-1 genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate Rax and Gsc when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).

Multiple myeloma (MM) is a neoplastic plasma-cell disorder characterized by clonal proliferation of malignant plasma cells. Despite extensive research, disease heterogeneity within and between treatment-resistant patients is poorly characterized. In the present study, we conduct a prospective, multicenter, single-arm clinical trial (NCT04065789), combined with longitudinal single-cell RNA-sequencing (scRNA-seq) to study the molecular dynamics of MM resistance mechanisms. Newly diagnosed MM patients (41), who either failed to respond or experienced early relapse after a bortezomib-containing induction regimen, were enrolled to evaluate the safety and efficacy of a daratumumab, carfilzomib, lenalidomide and dexamethasone combination. The primary clinical endpoint was safety and tolerability. Secondary endpoints included overall response rate, progression-free survival and overall survival. Treatment was safe and well tolerated; deep and durable responses were achieved. In prespecified exploratory analyses, comparison of 41 primary refractory and early relapsed patients, with 11 healthy subjects and 15 newly diagnosed MM patients, revealed new MM molecular pathways of resistance, including hypoxia tolerance, protein folding and mitochondria respiration, which generalized to larger clinical cohorts (CoMMpass). We found peptidylprolyl isomerase A (PPIA), a central enzyme in the protein-folding response pathway, as a potential new target for resistant MM. CRISPR–Cas9 deletion of PPIA or inhibition of PPIA with a small molecule inhibitor (ciclosporin) significantly sensitizes MM tumor cells to proteasome inhibitors. Together, our study defines a roadmap for integrating scRNA-seq in clinical trials, identifies a signature of highly resistant MM patients and discovers PPIA as a potent therapeutic target for these tumors. Integration of longitudinal single-cell analysis of relapsed and refractory multiple myeloma patients in a prospective clinical trial uncovers new pathways of drug resistance and identifies potential actionable targets.

Source code release

Limb development has long served as a model system for coordinated spatial patterning of progenitor cells. Here, we identify a population of naïve limb progenitors and show that they differentiate progressively to form the skeleton in a complex nonconsecutive three-dimensional pattern.Single-cell RNA sequencing of the developing mouse forelimb revealed three progenitor states: naïve, proximal and autopodial, as well as Msx1 as a marker for the naïve progenitors. In vivo lineage tracing confirmed this role and localized the naïve progenitors to the outer margin of the limb, along the anterior-posterior axis. Sequential pulse-chase experiments showed that the progressive transition of Msx1+ naïve progenitors into proximal and autopodial progenitors coincides with their differentiation to Sox9+ chondroprogenitors, which occurs along all the forming skeletal segments.Indeed, tracking the spatiotemporal sequence of differentiation showed that the skeleton forms progressively in a complex pattern. These findings suggest a new model for limb skeleton development.

Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes as tumors evolve could improve the precision of cancer treatment. Here we comprehensively analyze CAFs using index and transcriptional single-cell sorting at several time points along breast tumor progression in mice, uncovering distinct subpopulations. Notably, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immunoregulatory program to wound-healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAF changes over time in breast cancer progression, with direct clinical implications. Scherz-Shouval and colleagues characterize the dynamic changes in cancer-associated fibroblast subpopulations during breast cancer progression. They find that the ratio of S100A4+ and PDPN+ CAF subsets is associated with clinical outcome.

Comment in: DePICting T cell-APC crosstalk in cancer. Zhang T, Dong C. Nat Cancer. 2022 Mar;3(3):265-267. doi: 10.1038/s43018-022-00345-6. PMID: 35352059 No abstract available.; International audience; Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4+PD-1+CXCL13+ T cells are a major interacting hub with antigen-presenting cells in the tumor microenvironment of human non-small cell lung carcinoma. We define this clonally expanded, tumor-specific and conserved T-cell subset as T-helper tumor (Tht) cells. Reconstitution of Tht cells in vitro and in an ovalbumin-specific αβ TCR CD4+ T-cell mouse model, shows that the Tht program is primed in tumor-draining lymph nodes by dendritic cells presenting tumor antigens, and that their function is important for harnessing the antitumor response of anti-PD-1 treatment. Our molecular and functional findings support the modulation of Tht-dendritic cell interaction checkpoints as a major interventional strategy in immunotherapy.

SummaryDevelopment of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single cell RNAseq (scRNAseq) technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified novel gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells overexpress Lhx5 and Nkx2-1 genes, when exclusively interacting with the definitive endoderm (DE) cell. Moreover, there was a reciprocal impact on the transcriptome of the DE cells, as they tend to overexpress Rax and Gsc genes when in contact with the NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-tSNE to display the pseudo-spatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis with potential broader implication in other physiologically relevant processes.SignificancePhysical contact between neighboring cells is known to induce transcriptional changes in the interacting partners. Accurate measurement of these cell-cell contact based influences on the transcriptome is a very difficult experimental task. However, determining such transcriptional changes will highly enhance our understanding for the developmental processes. Current scRNAseq technology isolates the tissue into individual cells, making it hard to determine the potential transcriptomic changes due to its interacting partners. Here, we combined PIC-seq and computational algorithms to identify cell-type contact dependent transcriptional profiles focusing on endoderm development. We have computationally identified and experimentally validated specific gene expression patterns depending upon the presence of specific neighboring cell types. Our study suggests a new way to study cell-cell interactions for embryogenesis.

Significance Oral cancer is often diagnosed at late stages, resulting in a poor prognosis of this disease. Understanding early carcinogenic events taking place during the development of oral cancer is, therefore, an important unmet clinical need. This study identifies an early anticancer protective role of oral Langerhans cells (LCs), the principal mononuclear phagocyte involved in immunosurveillance of the oral epithelium. Continuous exposure to the carcinogen 4NQO hampers the local differentiation of LCs, while other mononuclear phagocytes and T cells with immunosuppressive characteristics populate the epithelium, leading to the development of oral cancer. These findings reveal an initial step by which immunosurveillance is compromised by a carcinogen, which could facilitate novel diagnostic approaches to better detection of oral cancer., Early diagnosis of oral squamous cell carcinoma (OSCC) remains an unmet clinical need. Therefore, elucidating the initial events of OSCC preceding tumor development could benefit OSCC prognosis. Here, we define the Langerhans cells (LCs) of the tongue and demonstrate that LCs protect the epithelium from carcinogen-induced OSCC by rapidly priming αβT cells capable of eliminating γH2AX+ epithelial cells, whereas γδT and natural killer cells are dispensable. The carcinogen, however, dysregulates the epithelial resident mononuclear phagocytes, reducing LC frequencies, while dendritic cells (DCs), macrophages, and plasmacytoid DCs (pDCs) populate the epithelium. Single-cell RNA-sequencing analysis indicates that these newly differentiated cells display an immunosuppressive phenotype accompanied by an expansion of T regulatory (Treg) cells. Accumulation of the Treg cells was regulated, in part, by pDCs and precedes the formation of visible tumors. This suggests LCs play an early protective role during OSCC, yet the capacity of the carcinogen to dysregulate the differentiation of mononuclear phagocytes facilitates oral carcinogenesis.

The endocrine pancreas is able to regenerate in response to insult, including by driving beta-cells into the cell division cycle. Until recently, communication between neighboring cells in islets of Langerhans was overlooked by single-cell genomic technologies, which require rigorous tissue dissociation into single cells. Here, we utilize sorting of physically interacting cells (PICs) with single-cell RNA-sequencing to systematically map cellular interactions in the regenerating endocrine pancreas. The cellular landscape of the regenerated pancreas features regeneration-associated endocrine populations.We explore the unexpected heterogeneity of beta-cells in regeneration, including an interaction-specific program between paired beta and delta-cells. Our analysis suggests that the particular cluster of beta-cells that pair with delta-cells benefits from stress protection, implying that the interaction between beta and delta-cells safeguards against regeneration-associated challenges.

Despite the clinical success of checkpoint inhibitors, a substantial gap still exists in our understanding of their mechanism of action. While antibodies to cytotoxic T lymphocyte-associated protein-4 (CTLA-4) were developed to block inhibitory signals in T cells, several recent studies have demonstrated that Fcγ receptor (FcγR)-dependent depletion of regulatory T cells (T

The functional activity and differentiation potential of cells are determined by their interactions with surrounding cells. Approaches that allow unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a tradeoff between spatial resolution and cell profiling depth. Here, we develop a photocage-based technology that allows isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including primary human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran) cage coupled to a set of nanobodies allows high-resolution photo-uncaging of different cell types in areas of interest. Single-cell RNA-sequencing of spatially defined CD8+ T cells is used to exemplify the feasibility of identifying location-dependent cell states. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples. The development of a photocage-nanobody based technology enabled in-depth analysis of live cells from tissues while retaining their spatial information.

Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4-9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2-3 d. Sequencing and processing the data through the analytical pipeline take another 1-2 d.

International audience; Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are prevalent liver conditions that underlie the development of life-threatening cirrhosis, liver failure and liver cancer. Chronic necro-inflammation is a critical factor in development of NASH, yet the cellular and molecular mechanisms of immune dysregulation in this disease are poorly understood. Here, using single-cell transcriptomic analysis, we comprehensively profiled the immune composition of the mouse liver during NASH. We identified a significant pathology-associated increase in hepatic conventional dendritic cells (cDCs) and further defined their source as NASH-induced boost in cycling of cDC progenitors in the bone marrow. Analysis of blood and liver from patients on the NAFLD/NASH spectrum showed that type 1 cDCs (cDC1) were more abundant and activated in disease. Sequencing of physically interacting cDC-T cell pairs from liver-draining lymph nodes revealed that cDCs in NASH promote inflammatory T cell reprogramming, previously associated with NASH worsening. Finally, depletion of cDC1 in XCR1DTA mice or using anti-XCL1-blocking antibody attenuated liver pathology in NASH mouse models. Overall, our study provides a comprehensive characterization of cDC biology in NASH and identifies XCR1+ cDC1 as an important driver of liver pathology.

The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

Natural killer (NK) cells and type 1 innate lymphoid cells (ILC1s) are heterogenous innate lymphocytes broadly defined in mice as Lin(−)NK1.1(+)NKp46(+) cells that express the transcription factor T-BET and produce interferon-γ. The ILC1 definition primarily stems from studies on liver and small intestinal populations. However, NK1.1(+)NKp46(+) cells in the salivary glands, uterus, adipose, and other tissues exhibit nonuniform programs that differ from those of liver or intestinal ILC1s or NK cells. Here, we performed single-cell RNA sequencing on murine NK1.1(+)NKp46(+) cells from blood, spleen, various tissues, and solid tumors. We identified gene expression programs of tissue-specific ILC1s, tissue-specific NK cells, and non-tissue-specific populations in blood, spleen, and other tissues largely corresponding to circulating cells. Moreover, we found that circulating NK cell programs were reshaped in tumor-bearing mice. Core programs of circulating and tumor NK cells paralleled conserved human NK cells signatures, advancing our understanding of the human NK-ILC1 spectrum.

The mechanisms controlling wiring of neuronal networks are not completely understood. The stereotypic architecture of the Drosophila mushroom body (MB) offers a unique system to study circuit assembly. The adult medial MB γ-lobe is comprised of a long bundle of axons that wire with specific modulatory and output neurons in a tiled manner, defining five distinct zones. We found that the immunoglobulin superfamily protein Dpr12 is cell-autonomously required in γ-neurons for their developmental regrowth into the distal γ4/5 zones, where both Dpr12 and its interacting protein, DIP-δ, are enriched. DIP-δ functions in a subset of dopaminergic neurons that wire with γ-neurons within the γ4/5 zone. During metamorphosis, these dopaminergic projections arrive to the γ4/5 zone prior to γ-axons, suggesting that γ-axons extend through a prepatterned region. Thus, Dpr12/DIP-δ transneuronal interaction is required for γ4/5 zone formation. Our study sheds light onto molecular and cellular mechanisms underlying circuit formation within subcellular resolution.

Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4

The functional activity and differentiation potential of cells is determined by their interaction with surrounding cells. Approaches that allow the unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a trade-off between spatial resolution and cell profiling depth. Here, we developed a photoswitch-based technology that allows the isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran)-cage coupled to nanobodies allowed photoswitching of cells in areas of interest with low-intensity violet light and without detectable phototoxicity. Single cell RNA sequencing of spatially defined CD8+ T cells was used to exemplify the feasibility of identifying location-dependent cell states at the single cell level. Finally, we demonstrate the efficient labeling and photoswitching of cells in live primary human tumor tissue. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped scRNA-seq data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus in an unsupervised manner. Applying Viral-Track to Bronchoalveloar-Lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human MetaPneumoVirus, present mainly in monocytes perturbed in type-I IFN-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology., Highlights Viral-track: a computational framework to analyze host-viral infection maps Viral-track sorts infected from bystander cells and reveals virus-induced expression SARS-CoV-2 infects epithelial cells and alters immune landscape in severe patients Coinfection of SARS-Cov-2 and hMPV affects monocytes and dampen interferon response, A computational framework that allows for the identification and characterizaton of virus-infected cells as well as bystander cell responses reveals how SARS-CoV-2 alters the immune responses of patients.

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are prevalent liver conditions that underlie the development of life-threatening cirrhosis, liver failure and liver cancer. Chronic necro-inflammation is a critical factor in development of NASH, yet the cellular and molecular mechanisms of immune dysregulation in this disease are poorly understood. Here, using single-cell transcriptomic analysis, we comprehensively profiled the immune composition of the mouse liver during NASH. We identified a significant pathology-associated increase in hepatic conventional dendritic cells (cDCs) and further defined their source as NASH-induced boost in cycling of cDC progenitors in the bone marrow. Analysis of blood and liver from patients on the NAFLD/NASH spectrum showed that type 1 cDCs (cDC1) were more abundant and activated in disease. Sequencing of physically interacting cDC-T cell pairs from liver-draining lymph nodes revealed that cDCs in NASH promote inflammatory T cell reprogramming, previously associated with NASH worsening. Finally, depletion of cDC1 in XCR1

Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes could improve the precision of cancer treatment. CAFs are usually analyzed at a single time-point using specific markers, and it is therefore unclear whether CAFs display plasticity as tumors evolve. Here, we analyze thousands of CAFs using index and transcriptional single-cell sorting, at several time-points along breast tumor progression in mice, uncovering distinct subpopulations. Strikingly, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immune-regulatory program to wound healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes, and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAFs changes over time in breast cancer progression, with direct clinical implications.

Summary Hematopoietic stem cells (HSCs) maintain life-long production of immune cells and can directly respond to infection, but sustained effects on the immune response remain unclear. We show that acute immune stimulation with lipopolysaccharide (LPS) induced only transient changes in HSC abundance, composition, progeny, and gene expression, but persistent alterations in accessibility of specific myeloid lineage enhancers occurred, which increased responsiveness of associated immune genes to secondary stimulation. Functionally, this was associated with increased myelopoiesis of pre-exposed HSCs and improved innate immunity against the gram-negative bacterium P. aeruginosa. The accessible myeloid enhancers were enriched for C/EBPβ targets, and C/EBPβ deletion erased the long-term inscription of LPS-induced epigenetic marks and gene expression. Thus, short-term immune signaling can induce C/EBPβ-dependent chromatin accessibility, resulting in HSC-trained immunity, during secondary infection. This establishes a mechanism for how infection history can be epigenetically inscribed in HSCs as an integral memory function of innate immunity.

Spatially resolved single-cell RNA sequencing (scRNAseq) is a powerful approach for inferring connections between a cell's identity and its position in a tissue. We recently combined scRNAseq with spatially mapped landmark genes to infer the expression zonation of hepatocytes. However, determining zonation of small cells with low mRNA content, or without highly expressed landmark genes, remains challenging. Here we used paired-cell sequencing, in which mRNA from pairs of attached mouse cells were sequenced and gene expression from one cell type was used to infer the pairs' tissue coordinates. We applied this method to pairs of hepatocytes and liver endothelial cells (LECs). Using the spatial information from hepatocytes, we reconstructed LEC zonation and extracted a landmark gene panel that we used to spatially map LEC scRNAseq data. Our approach revealed the expression of both Wnt ligands and the Dkk3 Wnt antagonist in distinct pericentral LEC sub-populations. This approach can be used to reconstruct spatial expression maps of non-parenchymal cells in other tissues.

Transcriptome profiling is widely used to infer functional states of specific cell types, as well as their responses to stimuli, to define contributions to physiology and pathophysiology. Focusing on microglia, the brain’s macrophages, we report here a side-by-side comparison of classical cell-sorting-based transcriptome sequencing and the ‘RiboTag’ method, which avoids cell retrieval from tissue context and yields translatome sequencing information. Conventional whole-cell microglial transcriptomes were found to be significantly tainted by artifacts introduced by tissue dissociation, cargo contamination and transcripts sequestered from ribosomes. Conversely, our data highlight the added value of RiboTag profiling for assessing the lineage accuracy of Cre recombinase expression in transgenic mice. Collectively, this study indicates method-based biases, reveals observer effects and establishes RiboTag-based translatome profiling as a valuable complement to standard sorting-based profiling strategies. Isolating tissue-resident cells runs the risk of altering their expression profile. Jung and colleagues use a RiboTagging approach to describe the microglial translatome and demonstrate that macrophage extraction from their tissue environment results in significant transcriptional alterations.

BackgroundHepatocellular carcinoma (HCC) has a dismal prognosis, and though checkpoint blocking antibodies have significantly improved patient outcome, many patients remain left out, highlighting the need to identify additional immune target to enhance therapeutic immunity. Macrophages (MF) are an abundant and heterogeneous population in the tumor microenvironment (TME), and are associated with a poor prognosis in multiple tumor types, including HCC, however, their molecular and functional diversity is still poorly understood.MethodsWe analyzed the molecular and spatial organization patterns of immune cells within the TME and adjacent tissue of 26 resected HCC lesions using single-cell RNA sequencing and multiplex immunohistochemistry (IHC).ResultsWe found that Kupffer cells, the self-renewing tissue-resident macrophages in liver tissue, are lacking from the TME, which is dominated by monocyte-derived macrophages. ScRNAseq followed by high-resolution clustering identified distinct MF molecular programs within the monocyte-derived macrophage compartment. One MF subset expressed a shared signature with monocytes including FCN1, S100A8 and T cell activation genes like CXCL9 and IL32. Conversely, one subset of MF expressed FOLR2, SEPP1 and genes of the complement (C1Qs), a program shared with KC in the adjacent tissue, and include another intratumoral subset enriched for the expression of TREM2 and GPNMB. Guided by these results, we are developing an IHC antibody panel that allows to visualize distinct MF localization in the TME. Intratumoral MF interface with, and potentially regulate, the T cell compartment within the TME. We are analyzing HCC tumor lesions in our treatment-naïve cohort and in patients treated with neoadjuvant anti-PD-1 therapy (NCT03916627) to study co-localization and direct interaction of MF and T cells using physically-interacting cell sequencing (PICseq). This analysis enables us to identify MF with direct cell-cell contact with T cells, and our preliminary analysis demonstrates an enrichment in MF with an immunosuppressive phenotype. We are also using spatial transcriptomic to map molecular programs of MF in the TME, that we will corroborated with additional patients.ConclusionsTaken together, our data provide a new understanding of intratumoral MF diversity and highlight the presence of specific immunoregulatory MF programs unique to tumor lesions, with subsets of these MF found to be directly interacting with T cells, potentially modulating anti-tumor responsiveness. Our analysis of resected tumor from anti-PD-1 treated patients, will allow us to correlate MF programs, and direct T cell interaction, with clinical response, and will inform therapeutic trials targeting specific MF populations so as to improve clinical efficacy of cancer immunotherapy.Trial RegistrationNCT03916627Ethics ApprovalSamples of tumor and non-involved liver were obtained from surgical specimens of patients undergoing resection at Mount Sinai Hospital (New York, NY) after obtaining informed consent in accordance with a protocol reviewed and approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai (RUTH Human Subjects Electronic Submission System 18–00407 and 20–04150) and in collaboration with the Biorepository and Department of Pathology.

Cancer associated fibroblasts (CAFs) are prevalent in carcinomas. CAFs are also heterogeneous and perform various tumor-promoting tasks. Understanding whether distinct CAF-subsets exert specific functions, and how the composition of CAFs changes as tumors evolve could improve the accuracy of cancer treatment. Here, we analyzed thousands of CAFs by single-cell RNA-sequencing and index-sorting at several timepoints along breast tumor progression in mice, revealing distinct CAF-subsets. We discovered that the transcriptional programs of these subsets change over time, shifting from an immune-regulatory program at earlier timepoints to wound-healing and antigen-presenting programs at later timepoints, indicating that the composition and functions of CAFs are dynamic. We also found the two main CAF subsets in human breast tumors, wherein their ratio was associated with disease outcome. This association was particularly correlated with BRCA mutations in triple-negative breast cancer. Our findings indicate that the diverse composition of CAFs in breast cancer changes over time as tumors progress, and that these changes are linked to disease outcome. Citation Format: Gil Friedman, Oshrat Levi-Galibov, Eyal David, Chamutal Bornstein, Amir Giladi, Maya Dadiani, Avi Mayo, Coral Halperin, Meirav Pevsner-Fischer, Hagar Lavon, Shimrit Mayer, Reinat Nevo, Yaniv Stein, Nora Balint-Lahat, Iris Barshack, H. Raza Ali, Carlos Caldas, Einav Nili Gal-Yam, Uri Alon, Ido Amit, Ruth Scherz-Shouval. Dynamic changes in the compositions of cancer associated-fibroblasts correlate with clinical outcome in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB009.