Cell-type Signatures Research Articles

Simultaneous visualization of membrane fluidity and morphology defines adhesion signatures of cancer cells

We developed an advanced optical microscope for the simultaneous visualization of membrane fluidity and morphology to define cell adhesion signatures. This microscope combines ratiometric spectral imaging of membrane fluidity and interferometric imaging of membrane morphology. As a preliminary demonstration, we simultaneously visualized the interface between a giant unilamellar vesicle (GUV) and a glass substrate at different temperatures. We identified more fluid regions of the membrane and membrane adhesion sites (conversely, low-fluidic, ordered membrane domains correlate with nonadhered regions). This microscopic system was applied to human breast cancer cell lines with different malignancies; then, we identified adhesion signature of cancer cells: 1) low-fluidic, ordered membrane domains at the cell periphery and 2) large fluidic deviation at the nonadhered region. Inhibition of the cholesterol synthesis pathway suppresses the ordered membrane domains at the cancer cell periphery; thus, high level of cholesterol supports the appearance. Furthermore, an inhibitor of the unsaturated lipid synthesis pathway suppressed the large fluidic deviation at the nonadhered region; variation of unsaturated lipids contributes to heterogeneity of the cancer membrane. Therefore, our advanced optical microscopy enables us to couple membrane physical properties with cell adhesion, leading to definition of adhesion signatures of broad cell types, not just for cancer cells, that regulate life phenomena.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

In situ molecular profiles of glomerular cells by integrated imaging mass spectrometry and multiplexed immunofluorescence microscopy

Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease. However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS, without sacrificing signal quality from either modality. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems.

Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA.

Sequencing-based spatial transcriptomics technologies have revolutionized our understanding of complex biological systems by enabling transcriptome profiling while preserving spatial context. However, spot-level expression measurements often amalgamate signals from diverse cells, obscuring potential heterogeneity. Existing methods aim to deconvolute spatial transcriptomics data into cell type proportions for each spot using single-cell RNA sequencing references but overlook cell-type-specific gene expression, essential for uncovering intra-type heterogeneity. We present PANDA (ProbAbilistic-based decoNvolution with spot-aDaptive cell type signAtures), a novel method that concurrently deciphers spot-level gene expression into both cell type proportions and cell-type-specific gene expression. PANDA integrates archetypal analysis to capture within-cell-type heterogeneity and dynamically learns cell type signatures for each spot during deconvolution. Simulations demonstrate PANDA's superior performance. Applied to real spatial transcriptomics data from diverse tissues, including tumor, brain, and developing heart, PANDA reconstructs spatial structures and reveals subtle transcriptional variations within specific cell types, offering a comprehensive understanding of tissue dynamics.

Molecular group and correlation guided structural learning for multi-phenotype prediction.

We propose a supervised learning bioinformatics tool, Biological gRoup guIded muLtivariate muLtiple lIneAr regression with peNalizaTion (Brilliant), designed for feature selection and outcome prediction in genomic data with multi-phenotypic responses. Brilliant specifically incorporates genome and/or phenotype grouping structures, as well as phenotype correlation structures, in feature selection, effect estimation, and outcome prediction under a penalized multi-response linear regression model. Extensive simulations demonstrate its superior performance compared to competing methods. We applied Brilliant to two omics studies. In the first study, we identified novel association signals between multivariate gene expressions and high-dimensional DNA methylation profiles, providing biological insights for the baseline CpG-to-gene regulation patterns in a Puerto Rican children asthma cohort. The second study focused on cell-type deconvolution prediction using high-dimensional gene expression profiles. Using Brilliant, we improved the accuracy for cell-type fraction prediction and identified novel cell-type signature genes.

Abstract B078: A comprehensive spatial atlas of neoadjuvant chemoradiation therapy in resected pancreatic cancer identifies cellular & microenvironmental determinants of persister populations

Abstract Background The molecular pathways involved in the response to radiation therapy (RT) in pancreatic ductal adenocarcinoma (PDAC) remain poorly understood. Despite extensive molecular stratification efforts, improvements in molecularly informed RT-based therapeutic strategies in the neoadjuvant setting have been limited. This study aims to elucidate the adaptive mechanisms and interactions within PDAC to identify new combinatorial therapeutic targets with RT. MethodsWe constructed a high-resolution molecular landscape of the cellular subtypes and spatial communities that constitute PDAC. Single-cell RNA sequencing (scRNA- seq) was performed on 16 PDAC biopsies of matched pre- and post-treatment samples from a Phase I/II dose-escalation clinical trial of pancreatic stereotactic body radiation therapy (SBRT). Additionally, Visium spatial transcriptomics (ST) was conducted on biopsies from primary PDAC patients collected at the time of surgery. These patients received either no neoadjuvant therapy (n = 8) or neoadjuvant chemoradiation (chemoRT) (n = 20). Cell type profiles were determined from scRNA-seq to compare changes in cell type proportions and signatures before and after RT. From these profiles, we employed robust cell type decomposition to analyze cell type signatures in our ST dataset. ResultsChemoRT induced several significant effects compared to controls within the tumor microenvironment, including an increase in the proportion of myofibroblasts, specifically senescent cancer-associated fibroblasts (CAFs), with signatures consistent with immune cell dysfunction. We observed an increase in CD8 and CD4 effector memory cells with RT; however, this occurred alongside a rise in immunosuppressive alternatively activated tumor-associated macrophages. Additionally, RT upregulated pathways related to HIF, cytokine production, B cell proliferation, and negative regulation of lymphocytes. Patients with increased infiltration of NK, memory B, and CD8 effector memory cells demonstrated improved overall survival outcomes. Inference of copy number variations enabled us to identify cancer subclones as either responsive or resistant in matched pre and post RT biopsies. Resistance was characterized by augmented hypoxia, KRAS activation, epithelial- mesenchymal transition, and interferon-gamma response signaling. Conversely, responders exhibited increased oxidative phosphorylation signaling and pathway enrichment associated with cell proliferation and cell cycle progression. Interestingly, RT induced a decrease in cancer cells characterized as harboring a chemoresistant basal-like molecular subtype. ConclusionThrough both longitudinal and therapy stratified tumor biopsies, we were able to observe significant selection pressures on stromal and cancer cells following RT. This includes upregulation of innate and adaptive immune pathways with a compensatory immunosuppressive response driven by myeloid cells and CAFs. This may provide opportunities for development of clinical trials to therapeutically target specific cellular phenotypes and their interactions. Citation Format: Vincent Bernard, Galia Jacobson, Kimal Rajapakshe, Jimin Min, Guangsheng Pei, Daniel Lin, Ayush Suresh, Dadi Jiang, Ching-Wei Tzeng, Manoop S Bhutani, Linghua Wang, Paola A Guerrero, Ethan B Ludmir, Albert C Koong, Anirban Maitra, Cullen M Taniguchi, Eugene J Koay. A comprehensive spatial atlas of neoadjuvant chemoradiation therapy in resected pancreatic cancer identifies cellular & microenvironmental determinants of persister populations [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B078.

ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

Interpretable single-cell factor decomposition using sciRED.

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

Interpretable single-cell factor decomposition using sciRED.

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity.

Colorectal cancer is a predominant malignancy with a second mortality worldwide. Despite its prevalence, therapeutic options remain constrained and surgical operation is still the most useful therapy. In this regard, a comprehensive spatially resolved quantitative proteome atlas was constructed to explore the functional proteomic landscape of colorectal cancer. This strategy integrates histopathological analysis, laser capture microdissection, and proteomics. Spatial proteome profiling of 200 tissue section samples facilitated by the fully integrated sample preparation technology SISPROT enabled the identification of more than 4000 proteins on the Orbitrap Exploris 240 from 2 mm2 × 10 μm tissue sections. Compared with normal adjacent tissues, we identified a spectrum of cancer-associated proteins and dysregulated pathways across various regions of colorectal cancer including ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Additionally, we conducted proteomic analysis on tumoral epithelial cells and paracancerous epithelium from early to advanced stages in hallmark rectum cancer and sigmoid colon cancer. Bioinformatics analysis revealed functional proteins and cell-type signatures associated with different regions of colorectal tumors, suggesting potential clinical implications. Overall, this study provides a comprehensive spatially resolved functional proteome landscape of colorectal cancer, serving as a valuable resource for exploring potential biomarkers and therapeutic targets.

AQP4 is upregulated in schizophrenia and Its inhibition attenuates MK-801-induced schizophrenia-like behaviors in mice

BackgroundThe pathophysiology and molecular mechanisms of schizophrenia (SCZ) remain unclear, and the effective treatment resources are still limited. The goal of this study is to identify the expression of AQP4 in SCZ patients and explore whether AQP4 inhibition could ameliorate schizophrenia-like behaviors and its mechanisms. MethodsMicroarray datasets of PFC compared with healthy control were searched in the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were analyzed with the GEO2R online tool. The Venny online tool and metascape online software were used to identify common abnormally expressed genes and conduct cell type signature enrichment analysis. SCZ mouse models were induced with MK-801, an NMDA receptor antagonist (intraperitoneal injection, 0.1 mg/kg/day for 7 days), and C6 cell models were treated with 100 μM MK-801. RT-qPCR, Western Blotting, and immunofluorescence were employed to determine the expression of AQP4, proinflammatory cytokines, and GFAP. Open field tests and social interaction tests were performed to evaluate the schizophrenia-like behaviors. ResultsBioinformatics analysis identified upregulation of AQP4 in the PFC of SCZ patients compared with healthy controls. Cell type signature enrichment analysis showed that all three DEGs lists were strongly enriched in the FAN EMBRYONIC CTX ASTROCYTE 2 category. Upregulation of AQP4 was also observed in MK-801-treated C6 cells and the PFC of MK-801-induced SCZ mouse model. Moreover, AQP4 inhibition with TGN-020 (an inhibitor of AQP4) improved anxiety-like behavior and social novelty preference defects in MK-801-treated mice. AQP4 inhibition also reduced the expression of IL-1β, IL-6, and TNF-α in MK-801-treated C6 cells and mouse model. ConclusionsAQP4 is upregulated in the PFC of SCZ patients compared with healthy controls. AQP4 inhibition could alleviate the anxiety-like behavior and social novelty defects in MK-801-treated mice, this may be due to the role of AQP4 in the regulation of the expression of proinflammatory cytokines.

Integrated sources model: A new space-learning model for heterogeneous multi-view data reduction, visualization, and clustering

In machine learning, multi-view data involve multiple distinct sets of attributes (“views”) for a common set of observations; when each view has the same attributes considered in different contexts, the data are said to contain multiple views of homogeneous format, which can be conceptualized as a tensor. In this article, we describe a novel approach for integrating multiple views of heterogeneous format into a common latent space using a workflow that involves non-negative matrix and tensor factorization (NMF/NTF). This approach, which we refer to as the integrated sources model (ISM), consists of two main steps: Embedding and analysis. In the embedding step, the views are transformed into matrices with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. We also present a variant of ISM; the integrated latent sources model (ILSM), which offers significant advantages over ISM in terms of computational power and in cases where the views are highly unbalanced with regard to the number of attributes per view. Noteworthy, ISM can be extended to process multi-omic and multi-view datasets even in the presence of missing views. We provide a proof-of-concept analysis using five examples, including the UCI Digits (the University of California Irvine Pen-Based Recognition of Handwritten Digits) dataset, a public cell-type gene signatures dataset, and a multi-omic single-cell dataset. These examples demonstrate that, in most cases, multi-view clustering is better achieved with ISM or its variant ILSM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to other approaches that involve latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging, spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked through a simple workflow implemented in Python.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Administration of intratumoral GD2-directed interleukin-2 immunocytokine and local radiation therapy to activate immune rejection of spontaneous canine melanoma.

Canine malignant melanoma provides a clinically relevant, large animal parallel patient population to study the GD2-reactive hu14.18-IL-2 immunocytokine as it is similar to human melanoma and expresses GD2. The objectives of this study were to evaluate safety, radiation fractionation, and identify informative biomarkers of an in-situ tumor vaccine involving local radiation therapy plus intratumoral-immunocytokine in melanoma tumor-bearing dogs. Twelve dogs (six dogs/arm) with locally advanced or metastatic melanoma were randomized to receive a single 8 Gy fraction (arm A) or three 8 Gy fractions over 1 week (arm B) to the primary site and regional lymph nodes (when clinically involved) with the single or last fraction 5 days before intratumoral-immunocytokine at 12 mg/m 2 on 3 consecutive days. Serial tumor biopsies were obtained. All 12 dogs completed protocol treatment, and none experienced significant or unexpected adverse events. Evidence of antitumor activity includes one dog with a complete response at day 60, one dog with a partial response at day 60, and four dogs with mixed responses. Histology of serial biopsies shows a variably timed increase in intratumoral lymphocytic inflammation in some dogs. Canine NanoString analyses of serial biopsies identified changes in gene signatures of innate and adaptive cell types versus baseline. There were no significant differences in NanoString results between arm A and arm B. We conclude that intratumoral-immunocytokine in combination with local radiation therapy in canine melanoma is well tolerated and has antitumor activity with the potential to inform clinical development in melanoma patients.

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BackgroundCurrent approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.ResultsWe cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.ConclusionsOur work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

Abstract 6169: High throughput in-situ spatial sequencing of proteins and RNA in FFPE tissue

Abstract We have developed an high throughput high resolution in situ multi-omic platform to enable studies in the rapidly evolving landscape of oncology and immunology research. Despite the potential of existing tools, the speed and throughput of current methodologies pose significant limitations. Here, we introduce a novel spatial sequencing platform employing a rapid 4-color SBS chemistry with sub-micron resolution imaging and an ultra-high throughput capacity. The platform simultaneously profiles RNA transcripts and proteins within formalin-fixed paraffin-embedded (FFPE) tissues. With up to 40 cm^2 of tissue area imaging within a 24-hour timeframe, the platform can power extensive, multi-omic studies in cancer research. To evaluate the performance of the platform, we prepared 5 um serial sections from healthy and tumor kidney FFPE block and used a novel tool to precisely position multiple tissue sections on a slide. Following probe binding and amplification, we used SBS readout to profile 105 transcripts and 6 proteins (via DNA-conjugated antibodies). Images were analyzed using a custom pipeline for feature detection and decoding, and cell segmentation using Cellpose. Results showed strong correlation to single-cell transcription profiles and cell type signatures defined by scRNA-Seq. Additionally, we see good agreement between single cell transcript and protein profiles. Existing spatial biology platforms are known to struggle with degraded and challenging clinical sample types (e.g. decalcified bone marrow), due to RNA fragmentation. We successfully profiled immune cells in FFPE-embedded bone marrow biopsies, measuring 143 transcript targets and 8 proteins. We believe this represents a significant step forward for in-situ analysis of bone marrow samples, and could be valuable for studying the pathology and response to treatment in blood cancers such as AML and CML. In-situ sequencing of antibody genes and T-cell receptors provides a deeper view of cellular immune response. As a step towards this goal, we demonstrated in-situ sequencing of antibody heavy and light chains within a B cell line, resulting in accurate readout of 28 bases within the CDR3 regions. In conclusion, we present an SBS-based approach for in-situ detection of targeted gene transcripts and proteins at an unprecedented throughput, and its application to the analysis of human kidney and bone marrow FFPE tissue samples. The capability to deeply profile tissues at high throughput could enable larger translational studies, and assembly of 3D maps of gene and protein expression at sub-cellular resolution. Citation Format: Michael Lawson, Yuji Ishitsuka, Andrew Pawlowski, Zhenmin Hong, Jake Koh, Kenneth Gouin, Nathan Ing, Richard Que, Yeoan Youn, Sara Ding, Tony Facchini, Ryan Costello, Katelyn Nelson, Jamie Stover, Howon Lee, Sarthak Duggal, Taylor Plaziak, Kaitlin Cameron, Mimi Abdu, Daan Witters, Martin Fabani, Eli Glezer. High throughput in-situ spatial sequencing of proteins and RNA in FFPE tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6169.

Single-cell omics identifies inflammatory signaling as a trans-differentiation trigger in mouse embryos

Trans-differentiation represents a direct lineage conversion; however, insufficient characterization of this process hinders its potential applications. Here, to explore a potential universal principal for trans-differentiation, we performed single-cell transcriptomic analysis of endothelial-to-hematopoietic transition (EHT), endothelial-to-mesenchymal transition, and epithelial-to-mesenchymal transition in mouse embryos. We applied three scoring indexes of entropies, cell-type signature transcription factor expression, and critical transition signals to show common features underpinning the fate plasticity of transition states. Cross-model comparison identified inflammatory-featured transition states and a common trigger role of interleukin-33 in promoting fate conversions. Multimodal profiling (integrative transcriptomic and chromatin accessibility analysis) demonstrated the inflammatory regulation of hematopoietic specification. Furthermore, multimodal omics and fate-mapping analyses showed that endothelium-specific Spi1, as an inflammatory effector, governs appropriate chromatin accessibility and transcriptional programs to safeguard EHT. Overall, our study employs single-cell omics to identify critical transition states/signals and the common trigger role of inflammatory signaling in developmental-stress-induced fate conversions.

Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.

Transcriptomic signatures of individual cell types in cerebral cavernous malformation

Cerebral cavernous malformation (CCM) is a hemorrhagic neurovascular disease with no currently available therapeutics. Prior evidence suggests that different cell types may play a role in CCM pathogenesis. The contribution of each cell type to the dysfunctional cellular crosstalk remains unclear. Herein, RNA-seq was performed on fluorescence-activated cell sorted endothelial cells (ECs), pericytes, and neuroglia from CCM lesions and non-lesional brain tissue controls. Differentially Expressed Gene (DEG), pathway and Ligand-Receptor (LR) analyses were performed to characterize the dysfunctional genes of respective cell types within CCMs. Common DEGs among all three cell types were related to inflammation and endothelial-to-mesenchymal transition (EndMT). DEG and pathway analyses supported a role of lesional ECs in dysregulated angiogenesis and increased permeability. VEGFA was particularly upregulated in pericytes. Further pathway and LR analyses identified vascular endothelial growth factor A/ vascular endothelial growth factor receptor 2 signaling in lesional ECs and pericytes that would result in increased angiogenesis. Moreover, lesional pericytes and neuroglia predominantly showed DEGs and pathways mediating the immune response. Further analyses of cell specific gene alterations in CCM endorsed potential contribution to EndMT, coagulation, and a hypoxic microenvironment. Taken together, these findings motivate mechanistic hypotheses regarding non-endothelial contributions to lesion pathobiology and may lead to novel therapeutic targets.Ddgp6MUP9uoWsxMj-KUiCyVideo

ScMD facilitates cell type deconvolution using single-cell DNA methylation references

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD’s superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer’s disease.