Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

High-Resolution Profiling of Human Vocal Fold Cellular Landscapes With Single-Nuclei RNA Sequencing

INTRODUCTION

The function of the vocal folds (VFs) is determined by the phenotype, abundance, and distribution of differentiated cells within specific microenvironments. Identifying this histologic framework is crucial in understanding laryngeal disease. A paucity of studies investigating VF cellular heterogeneity has been undertaken. Here, we examined the cellular landscape of human VFs by utilizing single-nuclei RNA-sequencing.

METHODS

Normal true VF tissue was excised from five patients undergoing pitch elevation surgery. Tissue was snap frozen in liquid nitrogen and subjected to cellular digestion and nuclear extraction. Nuclei were processed for single-nucleus sequencing using the 10X Genomics Chromium platform. Sequencing reads were assembled using cellranger and analyzed with the scanpy package in python.

RESULTS

RNA sequencing revealed 18 global cell clusters. While many were of epithelial origin, expected cell types, such as fibroblasts, immune cells, muscle cells, and endothelial cells were present. Subcluster analysis defined unique epithelial, immune, and fibroblast subpopulations.

CONCLUSION

This study evaluated the cellular heterogeneity of normal human VFs by utilizing single-nuclei RNA-sequencing. With further confirmation through additional spatial sequencing and microscopic imaging, a novel cellular map of the VFs may provide insight into new cellular targets for VF disease.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:3193-3200, 2024.

TNFR1-mediated senescence and lack of TNFR2-signaling limit human intervertebral disc cell repair in back pain conditions

Poor intervertebral disc (IVD) healing causes IVD degeneration (IVDD) and progression to herniation and back pain. This study identified distinct roles of TNFα-receptors (TNFRs) in contributing to poor healing in painful IVDD. We first isolated IVDD tissue of back pain subjects and determined the complex pro-inflammatory mixture contained many chemokines for recruiting inflammatory cells. Single-cell RNA-sequencing of human IVDD tissues revealed these pro-inflammatory cytokines were dominantly expressed by a small macrophage-population. Human annulus fibrosus (hAF) cells treated with IVDD-conditioned media (CM) underwent senescence with greatly reduced metabolic rates and limited inflammatory responses. TNFR1 inhibition partially restored hAF cell metabolism sufficiently to enable a robust chemokine and cytokine response to CM. We showed that the pro-reparative TNFR2 was very limited on hIVD cell membranes so that TNFR2 inhibition with blocking antibodies or activation using Atsttrin had no effect on hAF cells with CM challenge. However, TNFR2 was expressed in high levels on macrophages identified in scRNA-seq analyses, suggesting their role in repair responses. Results therefore point to therapeutic strategies for painful IVDD involving immunomodulation of TNFR1 signaling in IVD cells to enhance metabolism and enable a more robust inflammatory response including recruitment or delivery of TNFR2 expressing immune cells to enhance IVD repair.

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.

Interpretable deep learning in single-cell omics

MOTIVATION

Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations.

RESULTS

In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.

MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Cadherin-dependent adhesion is required for muscle stem cell niche anchorage and maintenance

Adhesion between stem cells and their niche provides stable anchorage and signaling cues to sustain properties such as quiescence. Skeletal muscle stem cells (MuSCs) adhere to an adjacent myofiber via cadherin-catenin complexes. Previous studies on N- and M-cadherin in MuSCs revealed that although N-cadherin is required for quiescence, they are collectively dispensable for MuSC niche localization and regenerative activity. Although additional cadherins are expressed at low levels, these findings raise the possibility that cadherins are unnecessary for MuSC anchorage to the niche. To address this question, we conditionally removed from MuSCs β- and γ-catenin, and, separately, αE- and αT-catenin, factors that are essential for cadherin-dependent adhesion. Catenin-deficient MuSCs break quiescence similarly to N-/M-cadherin-deficient MuSCs, but exit the niche and are depleted. Combined in vivo, ex vivo and single cell RNA-sequencing approaches reveal that MuSC attrition occurs via precocious differentiation, re-entry to the niche and fusion to myofibers. These findings indicate that cadherin-catenin-dependent adhesion is required for anchorage of MuSCs to their niche and for preservation of the stem cell compartment. Furthermore, separable cadherin-regulated functions govern niche localization, quiescence and MuSC maintenance.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Emerging Insights into Molecular Mechanisms of Inflammation in Myelodysplastic Syndromes

Inflammation impacts human hematopoiesis across physiologic and pathologic conditions, as signals derived from the bone marrow microenvironment, such as pro-inflammatory cytokines and chemokines, have been shown to alter hematopoietic stem cell (HSCs) homeostasis. Dysregulated inflammation can skew HSC fate-related decisions, leading to aberrant hematopoiesis and potentially contributing to the pathogenesis of hematological disorders such as myelodysplastic syndromes (MDS). Recently, emerging studies have used single-cell sequencing and muti-omic approaches to investigate HSC cellular heterogeneity and gene expression in normal hematopoiesis as well as in myeloid malignancies. This review summarizes recent reports mechanistically dissecting the role of inflammatory signaling and innate immune response activation due to MDS progression. Furthermore, we highlight the growing importance of using multi-omic techniques, such as single-cell profiling and deconvolution methods, to unravel MDSs' heterogeneity. These approaches have provided valuable insights into the patterns of clonal evolution that drive MDS progression and have elucidated the impact of inflammation on the composition of the bone marrow immune microenvironment in MDS.