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Solid tumors are spatially heterogeneous in their genetic, molecular and cellular composition, and this variation can be meaningful for diagnosis, prognosis and therapy. Recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we developed SlideCNA, a computational tool to extract sparse spatial CNA signals from spatial transcriptomics data, using expression-aware spatial binning. We test SlideCNA on simulated and real Slide-seq data of metastatic breast cancer (MBC) and demonstrate its potential for spatial sub-clone detection.

Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-Seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.

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.

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon–stimulated genes (ISGs) in monocytes, reduction of naïve CD4(+) T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH(+) CD8(+) T cells. Cell type–specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type–specific cis–expression quantitative trait loci and to link SLE-associated variants to cell type–specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE. INTRODUCTION: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with elevated prevalence in women and individuals of Asian, African, and Hispanic ancestry. Bulk transcriptomic profiling has implicated increased type 1 interferon signaling, dysregulated lymphocyte activation, and failure of apoptotic clearance as hallmarks of disease. Many genes participating in these processes are proximal to the ~100 loci associated with SLE. Despite this progress, a comprehensive census of circulating immune cells in SLE remains incomplete, and annotating the cell types and contexts that mediate genetic associations remains challenging. RATIONALE: Historically, flow cytometry and bulk transcriptomic analyses were used to profile the composition and gene expression of circulating immune cells in SLE. However, flow cytometry is biased by its use of a limited set of known markers, whereas bulk transcriptomic profiling does not have sufficient power to detect cell type–specific expression differences. Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) holds potential as a comprehensive and unbiased approach to simultaneously profile the composition and transcriptional states of circulating immune cells. However, application of scRNA-seq to population cohorts has been limited by sample throughput, cost, and susceptibility to technical variability. To overcome these limitations, we previously developed multiplexed scRNA-seq (mux-seq) to enable systematic and cost-effective scRNA-seq of population cohorts. RESULTS: We used mux-seq to profile more than 1.2 million PBMCs from 162 SLE cases and 99 healthy controls of either Asian or European ancestry. SLE cases exhibited differences in both the composition and state of PBMCs. Analysis of lymphocyte composition revealed a reduction in naïve CD4(+) T cells and an increase in repertoire-restricted GZMH(+) CD8(+) T cells. Analysis of transcriptomic profiles across eight cell types revealed that classical monocytes expressed the highest levels of both pan–cell type and myeloid-specific type 1 interferon-stimulated genes (ISGs). The expression of ISGs in monocytes was inversely correlated with naïve CD4(+) T cell abundance. Cell type–specific expression features accurately predicted case-control status and stratified patients into molecular subtypes. By integrating genotyping data and using a novel matrix decomposition method, we mapped shared and cell type–specific cis–expression quantitative trait loci (cis-eQTLs) across eight cell types. Cell type–specific cis-eQTLs were enriched for regions of open chromatin specific to the same or related cell types. Joint analysis of cis-eQTLs and genome-wide association study results enabled identification of cell types relevant to immune-mediated diseases, fine-mapping of disease-associated loci, and discovery of novel SLE associations. Interaction analysis identified variants whose effects on gene expression are further modified by interferon activation across patients. CONCLUSION: SLE remains challenging to diagnose and treat. The heterogeneity of disease manifestations and treatment response highlight the need for improved molecular characterization. In a large multiethnic cohort, we demonstrate mux-seq as a systematic approach to characterize cellular composition, identify cell type–specific transcriptomic signatures, and annotate genetic variants associated with SLE.

Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases., A set of ready-to-use tools for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq facilitates the implementation of single-cell technologies in clinical settings and the construction of single-cell tumor atlases.

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

Mouse models are a tool for studying the mechanisms underlying complex diseases; however, differences between species pose a significant challenge for translating findings to patients. Here, we used single-cell transcriptomics and orthogonal validation approaches to provide cross-species taxonomies, identifying shared broad cell classes and unique granular cellular states, between mouse and human kidney. We generated cell atlases of the diabetic and obese kidney using two different mouse models, a high-fat diet (HFD) model and a genetic model (BTBR ob/ob), at multiple time points along disease progression. Importantly, we identified a previously unrecognized, expanding Trem2high macrophage population in kidneys of HFD mice that matched human TREM2high macrophages in obese patients. Taken together, our cross-species comparison highlights shared immune and metabolic cell-state changes.

This protocol describes the dissociation of primary human colorectal cancer (CRC) resection specimens into single cell suspensions for single cell RNA-seq applications. It was successfully used for droplet-based single cell RNA-seq of more than 60 patient specimens using the 10x Genomics platform, including 21 HTAPP CRC cohort samples. It can also be used upstream of other single cell RNA-seq approaches such as plate-based SS2 single cell RNA-seq protocols.

This protocol was used as part of the Human Tumor Atlas Pilot Project’s (HTAPP) efforts to establish a working protocol for the dissociation of human neuroblastoma core needle biopsies to a single-cell suspension compatible with droplet-based single-cell RNA-Seq technology. It is a testing protocol and we do not recommended using it for the intended purpose. Instead, for processing human neuroblastoma tumors, we recommend using the papain-based protocol established by Patel et al.

This protocol was used as part of the Human Tumor Atlas Pilot Project’s (HTAPP) efforts to establish a working protocol for the dissociation of human neuroblastoma core needle biopsies to a single-cell suspension compatible with droplet-based single-cell RNA-Seq technology. It is a testing protocol and we do not recommended using it for the intended purpose. Instead, for processing human neuroblastoma tumors, we recommend using the papain-based protocol established by Patel et al.

Identifying gene regulatory targets of nuclear proteins in tissues remains a challenge. Here we describe intranuclear Cellular Indexing of Transcriptomes and Epitopes (inCITE-seq), a scalable method for measuring multiplexed intranuclear protein levels and the transcriptome in parallel in thousands of cells, enabling joint analysis of TF levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed the genome-wide effect of each TF and recovered known targets. Cell type-specific genes associated with each TF were co-expressed as distinct modules that each corresponded to positive or negative TF levels, showing that our approach can disentangle relative contributions of TFs to gene expression and add interpretability to gene networks. InCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

Identifying gene-regulatory targets of nuclear proteins in tissues is a challenge. Here we describe intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq), a scalable method that measures multiplexed intranuclear protein levels and the transcriptome in parallel across thousands of nuclei, enabling joint analysis of transcription factor (TF) levels and gene expression in vivo. We apply inCITE-seq to characterize cell state-related changes upon pharmacological induction of neuronal activity in the mouse brain. Modeling gene expression as a linear combination of quantitative protein levels revealed genome-wide associations of each TF and recovered known gene targets. TF-associated genes were coexpressed as distinct modules that each reflected positive or negative TF levels, showing that our approach can disentangle relative putative contributions of TFs to gene expression and add interpretability to inferred gene networks. inCITE-seq can illuminate how combinations of nuclear proteins shape gene expression in native tissue contexts, with direct applications to solid or frozen tissues and clinical specimens.

Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory disease. Characterizing PDAC by mRNA profiling remains particularly challenging. Previously identified bulk expression subtypes were influenced by contaminating stroma and have not yet informed clinical management, whereas single cell RNA-seq (scRNA-seq) of fresh tumors under-represented key cell types. Here, we developed a robust single-nucleus RNA-seq (snRNA-seq) technique for frozen archival PDAC specimens and used it to study both untreated tumors and those that received neoadjuvant chemotherapy and radiotherapy (CRT). Gene expression programs learned across untreated malignant cell and fibroblast profiles uncovered a clinically relevant molecular taxonomy with improved prognostic stratification compared to prior classifications. Moreover, in the increasingly-adopted neoadjuvant treatment context, there was a depletion of classical-like phenotypes in malignant cells in favor of basal-like phenotypes associated with TNF-NFkB and interferon signaling as well as the presence of novel acinar and neuroendocrine classical-like states, which may be more resilient to cytotoxic treatment. Spatially-resolved transcriptomics revealed an association between malignant cells expressing these basal-like programs and higher immune infiltration with increased lymphocytic content, whereas those exhibiting classical-like programs were linked to sparser macrophage-predominant microniches, perhaps pointing to susceptibility to distinct therapeutic strategies. Our refined molecular taxonomy and spatial resolution can help advance precision oncology in PDAC through informative stratification in clinical trials and insights into differential therapeutic targeting leveraging the immune system.

SARS-CoV-2, the coronavirus that causes COVID-19, binds to angiotensin-converting enzyme 2 (ACE2) on human cells. Beyond the lung, COVID-19 impacts diverse tissues including the kidney. ACE2 is a key member of the Renin-Angiotensin-Aldosterone System (RAAS) which regulates blood pressure, largely through its effects on the kidney. RAAS blockers such as ACE inhibitors (ACEi) and Angiotensin Receptor Blockers (ARBs) are widely used therapies for hypertension, cardiovascular and chronic kidney diseases, and therefore, there is intense interest in their effect on ACE2 expression and its implications for SARS-CoV-2 pathogenicity. Here, we analyzed single-cell and single-nucleus RNA-seq of human kidney to interrogate the association of ACEi/ARB use with ACE2 expression in specific cell types. First, we performed an integrated analysis aggregating 176,421 cells across 49 donors, 8 studies and 8 centers, and adjusting for sex, age, donor and center effects, to assess the relationship of ACE2 with age and sex at baseline. We observed a statistically significant increase in ACE2 expression in tubular epithelial cells of the thin loop of Henle (tLoH) in males relative to females at younger ages, the trend reversing, and losing significance with older ages. ACE2 expression in tLoH increases with age in females, with an opposite, weak effect in males. In an independent cohort, we detected a statistically significant increase in ACE2 expression with ACEi/ARB use in epithelial cells of the proximal tubule and thick ascending limb, and endothelial cells, but the association was confounded in this small cohort by the underlying disease. Our study illuminates the dynamics of ACE2 expression in specific kidney cells, with implications for SARS-CoV-2 entry and pathogenicity.

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells2. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape3-5 and provides a resource for the development of novel therapeutic approaches.

This protocol describes amethodbased on work by Drokhlyansky et al.for nuclei isolation from frozen tissue.It can be used on both healthy and disease tissues and is compatible with droplet-based single-nucleus RNA-Seq technologySlyper et al. This method is part of a toolbox for processing frozen tissue samples forsingle-nucleus RNA-Seq, including the CST (this protocol), TST and NST protocols (all available inprotocols.io). We recommend users to test all three protocols on their tissue type of interest and perform side-by-side comparison of the data generated. A description of the complete toolbox andguidance for testing and selecting methods from the toolbox for processing other tumors can be found in Slyper et al. For the Human Tumor Atlas Pilot Project (HTAPP), the CST-nuclei isolation method was used to profile pediatric glioma frozen samples. It was also tested on metastatic breast cancer, ovarian cancer, pediatric sarcoma, and pediatric neuroblastoma frozen samples. For these cancer types, however, TST was the protocol of choice.

This protocol is used for the dissociation of human metastatic breast cancer core needle biopsies to a single-cell suspension compatible with droplet-based single-cell RNA-Seq technology (Slyper et al). For the Human Tumor Atlas Pilot Project (HTAPP) it has been successfully applied to biopsies collected from breast, liver and lymph node. Description of this protocol and guidance for testing and selecting methods for processing other tumor and sample types can be found in Slyper et al.

This protocol is used for the dissociation of human primary lung cancer resections to a single-cell suspension compatible with droplet-based single-cell RNA-Seq technology. For the Human Tumor Atlas Pilot Project (HTAPP) it has been successfully applied to non-small cell adenocarcinoma from which it was able to capture a diversity of cell types, including malignant (epithelial) and non malignant cells such asmesenchymal, endothelial, myeloid, and lymphoid cells. In some instances, this protocol can be combined with a protocol for CD45-cell depletion, which is used for fast depletion of immune cells for enrichment of malignant and stromal cells. Description of this protocol and guidance for testing and selecting methods for processing other tumor and sample types can be found in Slyper et al.

Methods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across mouse visual cortex; and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus ExSeq enables highly multiplexed mapping of RNAs, from nanoscale to system scale.One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

Metastatic breast cancer (MBC) remains incurable due to inevitable development of therapeutic resistance. Although tumor cell intrinsic mechanisms of resistance in MBC are beginning to be elucidated by bulk sequencing studies, the roles of the tumor microenvironment and intratumor heterogeneity in therapeutic resistance remain underexplored due to both technological barriers and limited availability of samples. To comprehensively capture these characteristics we have adapted a research biopsy protocol to collect tissue for an array of single-cell and spatio-molecular assays whose performance we have optimized for MBC, including single-cell and single-nucleus RNA sequencing, Slide-Seq, Multiplexed Error-Robust FISH (MERFISH), Expansion Sequencing (ExSEQ), Co-detection by Indexing (CODEX) and Multiplexed Ion Beam Imaging (MIBI). To date, we have successfully performed single-cell or single-nucleus RNAseq in 67 MBC biopsies and generated detailed accompanying clinical annotations for each. These samples provide a representation of the clinicopathological diversity of MBC including different breast cancer subtypes (44 HR+/HER2-, 3 HR-/HER2+, 3 HR+/HER2+, 16 TNBC, 1 unknown), common anatomic sites of metastasis (37 liver, 9 axilla, 7 breast, 5 bone, 3 chest wall, 3 neck, 1 brain, 1 lung, 1 skin), metastatic presentations (53 recurrent, 14 de novo) and histologic subtypes in the breast (45 IDC, 7 ILC, 6 mixed, 3 DCIS, 1 mucinous, 5 unknown/NA). Following optimization, both single-cell and single-nucleus RNA seq perform well in these MBC biopsies recovering all expected cell types including the malignant, stromal (e.g. fibroblasts, endothelial cells), myeloid (e.g. monocytes, macrophages) and lymphoid compartments (e.g. T cells, B cells, NK cells) as well as relevant oncogenic programs (e.g. cell cycle programs in all compartments; EMT-like and ER signaling programs in the malignant compartment, immune checkpoint programs in the lymphoid compartment; and fibroblast activation and vascular homeostasis programs in the stromal compartment). In addition to differences between the two techniques, these data demonstrate substantial intratumor heterogeneity in cell type composition. For example in liver biopsies the average number of cells per sample compartment by single nucleus RNA-seq was 6745 malignant (56%, SD 4216), 4637 stromal (41%, SD 3727), 1196 lymphoid (8%, SD 1617) and 874 myeloid (6%, SD 852); in breast biopsies the average number of cells per compartment by single nucleus RNA-seq was 6421 malignant (70%, SD 3497), 1628 stromal (24%, SD 117), 333 lymphoid (4%, SD 170) and 213 myeloid (3%, SD 117). Additionally, we find both inter- and intra-tumor heterogeneity in expression patterns and programs including, for example, expression of ER, PR and HER2 within clinical receptor subtypes (log normalized counts for ER expression in tumor cells by single cell RNA-seq: HR+/HER2- 0.921 (SD 0.714); HR+/HER2+ 0.768 (SD 0.624); HR-/HER2+ 0.018 (SD 0.122); and HR-/HER2- 0.005 (SD 0.066). For a subset of 13 biopsies we are also completing the spatiomolecular characterization methods on serial sections of a single adjacent biopsy. This unique experimental setup was designed to enable efficient comparison and integration of these assays. In spite of differences between experimental techniques and readouts, cell typing can be approached by annotation transfer from matching single cell or single nucleus RNAseq data, enabling exploratory analyses including evaluation of spatial phenotypes and cell type colocalization. Overall, these single cell and spatial data afford a comprehensive atlas including cell types, cell states/programs, cell interactions and spatial organization in MBC lesions. Future analyses will include serial biopsies over time and integration of clinicopathologic data including therapeutic response and resistance. Citation Format: Daniel L Abravanel, Johanna Klughammer, Timothy Blosser, Yury Goltsev, Sizun Jiang, Yunjao Bai, Evan Murray, Shahar Alon, Yi Cui, Daniel R Goodwin, Anubhav Sinha, Ofir Cohen, Michal Slyper, Orr Ashenberg, Danielle Dionne, Judit Jané-Valbuena, Caroline BM Porter, Asa Segerstolpe, Julia Waldman, Sébastien Vigneau, Karla Helvie, Allison Frangieh, Laura DelloStritto, Miraj Patel, Jingyi We, Kathleen Pfaff, Nicole Cullen, Ana Lako, Madison Turner, Isaac Wakiro, Sara Napolitano, Abhay Kanodia, Rebecca Ortiz, Colin MacKichan, Stephanie Inga, Judy Chen, Aaron R Thorner, Asaf Rotem, Scott Rodig, Fei Chen, Edward S Boyden, Garry P Nolan, Xiaowei Zhuang, Orit Rozenblatt-Rosen, Bruce E Johnson, Aviv Regev, Nikhil Wagle. Spatio-molecular dissection of the breast cancer metastatic microenvironment [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD6-03.

This protocol describes the dissociation of primary human colorectal cancer (CRC) resection specimens into single cell suspensions for single cell RNA-seq applications. It was successfully used for droplet-based single cell RNA-seq of more than 60 patient specimens using the 10x Genomics platform, including 20 HTAPP CRC cohort samples. It can also be used upstream of other single cell RNA-seq approaches such as plate-based SS2 single cell RNA-seq protocols.

Synthesis, characterization, and applications of strongly fluorescent, multicolored protein nanoparticles (GlowDots) are reported here. Bovine serum albumin was cross-linked under controlled conditions to form nanoparticles, where particle size was controlled from 20 to 100 ± 10 nm by choosing appropriate reaction conditions. The absorption as well as the emission wavelengths were controlled without changing the particle size, unlike quantum dots. Each GlowDot was loaded with up to 214 ± 50 chromophores, and hence, the particles have high molar absorptivities (106 M–1 cm–1) as well as high brightness (105 to 106 M–1 cm–1). A large number of functional groups cover the particle surface and these are further functionalized to enhance cellular uptake. GlowDots that were labeled with fluorescein and functionalized with taurine, for example, were quickly taken up by HeLa, MDA-MB-231, PC3, and L6 myoblast cells, as interrogated by fluorescence imaging studies. GlowDots were biocompatible, size tunable, biodegra...

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd individuals. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across tumors from affected individuals and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T cell-attracting chemokines. By identifying interacting cellular programs, we reveal the logic underlying spatially organized immune-malignant cell networks.

Human iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we profile four human iPSC lines for a total of 450,118 single cells to show how organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes are largely reproducible across time points, protocols, and replicates, we detect variability in cell proportions between different iPSC lines, largely due to off-target cells. To address this, we analyze organoids transplanted under the mouse kidney capsule and find diminished off-target cells. Our work shows how single cell RNA-seq (scRNA-seq) can score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells., How reproducible human kidney organoids derived from different iPSC lines are, and how faithful they are to human kidney tissue remain unclear. Here, the authors use four human iPSC lines to derive kidney organoids and show how organoid composition is reproducible, comparable to human tissue and of improved quality after transplantation.

Single cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.

Genome-wide association studies (GWAS) have revealed risk alleles for ulcerative colitis (UC), but their cell type and pathway specificities are often unknown. Here, we generate an atlas of 115,517 cells from the colon mucosa of seven UC patients and ten healthy individuals, revealing 51 epithelial, stromal, and immune cell subsets. These included a subset of BEST4+ enterocytes, which may sense and respond to pH, and IL13RA2+IL-11+ inflammatory fibroblasts, which we associate with resistance to anti-TNF therapy. Inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and CD8+IL-17+ T cells expand during disease, and form intercellular interaction hubs that mediate cross-talk between diverse cellular lineages. We identify hundreds of putative autocrine and paracrine cell-cell interactions that may explain the migration, expansion, or inhibition of cell types with disease. Surprisingly, UC risk genes are often cell type specific and co-regulated in relatively few gene modules, suggesting convergence onto limited sets of cell types and pathways. Using this observation, we nominate and infer putative functions for UC risk genes across all GWAS loci. Our atlas thus provides a framework for interrogating complex human diseases and mapping risk variants onto their cell types and pathways of activity.

This protocol describes a robust and easy method for nuclei isolation for frozen tissues based on unpublished work by Drokhlyansky et al. This protocol is compatible with 10X droplet-based single-cell technology. This method is part of a toolbox for processing frozen tissue samples, including the TST (this protocol), CST and NST protocols (all available in protocols.io). We recommend users to test all three protocols on their tissue type of interest, and perform side-by-side comparison of the data generated. For the HTAPP project, this protocol was tested and validated for metastatic breast cancer, ovarian cancer, pediatric neuroblastoma and pediatric sarcoma frozen samples.

This protocol is used for the dissociation of human primary lung cancer resections to a single-cell suspension compatible with loading into 10x Genomics Single-Cell RNA-seq system. It has been successfully applied to Non-Small Cell Adenocarcinoma from which it was able to capture a diversity of cell types, including epithelial/tumor, mesenchymal, endothelial, myeloid, and lymphoid cells.

This protocol has been successfully used for the dissociation of a human metastatic breast cancer axillary lymph node excision biopsy to a single-cell suspension followed by single-cell RNA sequencing using the 10x Genomics system.

Human iPSC-derived kidney organoids have the potential to revolutionize discovery, but assessing their consistency and reproducibility across iPSC lines, and reducing the generation of off-target cells remain an open challenge. Here, we used single cell RNA-Seq (scRNA-Seq) to profile 415,775 cells to show that organoid composition and development are comparable to human fetal and adult kidneys. Although cell classes were largely reproducible across iPSC lines, time points, protocols, and replicates, cell proportions were variable between different iPSC lines. Off-target cell proportions were the most variable. Prolongedin vitroculture did not alter cell types, but organoid transplantation under the mouse kidney capsule diminished off-target cells. Our work shows how scRNA-seq can help score organoids for reproducibility, faithfulness and quality, that kidney organoids derived from different iPSC lines are comparable surrogates for human kidney, and that transplantation enhances their formation by diminishing off-target cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.

While recent studies have begun to elucidate the genomics of metastatic breast cancer (MBC), the transcriptional programs (progs) that drive the drug-resistant phenotype remain poorly understood. We prospectively collected biopsies from patients with MBC with detailed clinicopathologic features, including treatment and response characteristics. We profiled 29 biopsies by single-cell RNA seq, as well as bulk RNA-seq and whole exome sequencing on an additional 207 biopsies (analyzed snapshot, ongoing study). We analyzed these data to generate an atlas, delineating the cell-types, cell-states, and transcriptional progs. We profiled 100,470 single cell transcriptomes and generated a comprehensive MBC atlas of the tumor and tumor microenvironment (TME). We next inferred cell types and progs associated with clinicopathologic characteristics. For example, we found significant differences in the TME of liver metastases compared to other sites, consistent with immunosuppression in the hepatic space. In particular, liver metastases were depleted in activated B-cells (lower CD69, TCL1A, VPREB3, BCNP1, and several activation-related chemokines - CXCR4/5/6/7), and their infiltrating T-cells expressed lower levels of effector and cytotoxicity markers (table=T) including CD8, CD3, beta chemokines CCL4/CCL5, IFI16, PRF1, granzymes, and GNLY, and exceptionally low expression of antigen processing genes (T). To increase our power to make clinically relevant associations, we performed a joint analysis of the single-cell and bulk RNA-Seq data, to identify malignant cell progs related to specific oncogenic mutations, with implications for metastatic and drug-resistance phenotypes. For example, we characterized the oncogenic prog associated with activating estrogen receptor mutation (ESR1-mut). As expected, ESR1-mut prog overlaps with many known ER and luminal B markers (T). ESR1-mut prog also included specific Interferon-stimulated genes (ISGs) - IFI6, ISG15, IFIT1, STAT1, which are associated with tamoxifen resistance (T) and ECM-mediated regulation of apoptosis (T). These ISGs are predictive of poor prognosis among endocrine-treated patients (HR=1.69. p=1e-04, n=929, kmplotter). The ESR1-mut prog also included genes associated with cell-migration (SOX9, AGR2, TXNIP and several S100 genes). This suggests a role for ESR1 mutation in pathogenicity, beyond ligand-independent activation of ER signaling. A second malignant cell prog was associated with RB-null. While most of these genes were highly correlated with cell-cycle (R=0.4), some do not (HINT1, RHOC, SNRPB, ANAPC11, TMEM208, POLR2J). In spite of decoupling from proliferation, these still predict of poor outcome across breast cancer (HR=1.41, p= 6.0e-10, n=3951, kmplotter). We characterized the prog of high-grade tumors in our cohort, and found a strong association with RB-null prog (T). Similarly, we recovered additional mutation-specific oncogenic progs, including for TP53, GATA3, FOXA1, HER2, and FGFR mutants, forming a compendium of in-vivo oncogenic signatures. To the best of our knowledge these data represent the first integration of single cell and bulk RNA-seq data in MBC, resulting in a comprehensive single-cell-resolution transcriptional atlas, and a catalog of drug-resistance oncogenic progs with implications for immunotherapy and precision-oncology. Single-cell In-vivo programComparison Gene setAssociationP-value(top genes)(MSigDB name, PubMed ID, when applicable)Odds-ratio(Fisher''''s Exact test, two-sided)Depleted in liver T-cellsEffectors-cells markers4.077.29E-07(compared to other metastatic site - Bone, Skin, Chest-wall, Lymph node, Breast-met)(PMID: 28052254)""Antigen processing markers32.17.26E-07(PMID: 28052254)ESR1-mutVANTVEER_BREAST_CANCER_ESR1_UP5.82.30E-11(Enriched in ESR1-mutatated)(PMID: 11823860)“SMID_BREAST_CANCER_LUMINAL_B_UP5.898.10E-12“”DOANE_BREAST_CANCER_ESR1_UP6.30812.10E-09""BOWIE_RESPONSE_TO_TAMOXIFEN10.360.00036(PMID 17016442)""BECKER_TAMOXIFEN_RESISTANCE_UP7.81.20E-06(PMID 15657362)""BOWIE_RESPONSE_TO_EXTRACELLULAR_MATRIX8.240.0028(PMID 17016442)Enriched in RB-nullEnriched in High-grade (from our study)23.97.50E-05 Citation Format: Ofir Cohen, Daniel Abravanel, Michal Slyper, Johanna Klughammer, Judit Jane-Valbuena, Karla Helvie, Laura Dellostritto, Allison Frangieh, Sebastien Vigneau, Jingyi Wu, Angie Mayorga, Julia Waldman, Lan Nguyen, Danielle Dionne, Orr Ashenberg, Matan Hofree, Mike Cuoco, Christopher Rodman, Eric P Winer, Nancy Lin, Bruce Johnson, Asaf Rotem, Orit Rozenblatt-Rosen, Aviv Regev, Nikhil Wagle. A joint atlas of single-cell and bulk RNA-seq in metastatic breast cancer allows inference of oncogenic and drug-resistant transcriptional programs in malignant cells and the tumor microenvironment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS6-05.

Single-nucleus RNA-Seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate, including frozen clinical samples. This opens the way, in principle, to large studies, such as those required for human genetics, clinical trials, or precise cell atlases of large organs. However, such applications are currently limited by batch effects, sequential processing, and costs. To address these challenges, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies against the nuclear pore complex to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled in multiplex with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter the recovered transcriptome profiles. We further developed demuxEM, a novel computational tool that robustly detects inter-sample nucleus multiplets and assigns singlets to their samples of origin by antibody barcodes, and validated its accuracy using gender-specific gene expression, species-mixing and natural genetic variation. Nucleus hashing significantly reduces cost per nucleus, recovering up to about 5 times as many single nuclei per microfluidc channel. Our approach provides a robust technique for diverse studies including tissue atlases of isogenic model organisms or from a single larger human organ, multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.