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.

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, 2024.

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

We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.

The Breast Cancer Single-Cell Atlas: Defining cellular heterogeneity within model cell lines and primary tumors to inform disease subtype, stemness, and treatment options.

PURPOSE: Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions. METHODS: We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors. RESULTS: Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies. CONCLUSION: The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.

Klebsiella pneumoniae induces host metabolic stress that promotes tolerance to pulmonary infection.

K. pneumoniae sequence type 258 (Kp ST258) is a major cause of healthcare-associated pneumonia. However, it remains unclear how it causes protracted courses of infection in spite of its expression of immunostimulatory lipopolysaccharide, which should activate a brisk inflammatory response and bacterial clearance. We predicted that the metabolic stress induced by the bacteria in the host cells shapes an immune response that tolerates infection. We combined in situ metabolic imaging and transcriptional analyses to demonstrate that Kp ST258 activates host glutaminolysis and fatty acid oxidation. This response creates an oxidant-rich microenvironment conducive to the accumulation of anti-inflammatory myeloid cells. In this setting, metabolically active Kp ST258 elicits a disease-tolerant immune response. The bacteria, in turn, adapt to airway oxidants by upregulating the type VI secretion system, which is highly conserved across ST258 strains worldwide. Thus, much of the global success of Kp ST258 in hospital settings can be explained by the metabolic activity provoked in the host that promotes disease tolerance.

Integration of Single-Cell Transcriptomics With a High Throughput Functional Screening Assay to Resolve Cell Type, Growth Kinetics, and Stemness Heterogeneity Within the Comma-1D Cell Line.

Cell lines are one of the most frequently implemented model systems in life sciences research as they provide reproducible high throughput testing. Differentiation of cell cultures varies by line and, in some cases, can result in functional modifications within a population. Although research is increasingly dependent on these in vitro model systems, the heterogeneity within cell lines has not been thoroughly investigated. Here, we have leveraged high throughput single-cell assays to investigate the Comma-1D mouse cell line that is known to differentiate in culture. Using scRNASeq and custom single-cell phenotype assays, we resolve the clonal heterogeneity within the referenced cell line on the genomic and functional level. We performed a cohesive analysis of the transcriptome of 5,195 sequenced cells, of which 85.3% of the total reads successfully mapped to the mm10-3.0.0 reference genome. Across multiple gene expression analysis pipelines, both luminal and myoepithelial lineages were observed. Deep differential gene expression analysis revealed eight subclusters identified as luminal progenitor, luminal differentiated, myoepithelial differentiated, and fibroblast subpopulations-suggesting functional clustering within each lineage. Gene expression of published mammary stem cell (MaSC) markers Epcam, Cd49f, and Sca-1 was detected across the population, with 116 (2.23%) sequenced cells expressing all three markers. To gain insight into functional heterogeneity, cells with patterned MaSC marker expression were isolated and phenotypically investigated through a custom single-cell high throughput assay. The comparison of growth kinetics demonstrates functional heterogeneity within each cell cluster while also illustrating significant limitations in current cell isolation methods. We outlined the upstream use of our novel automated cell identification platform-to be used prior to single-cell culture-for reduced cell stress and improved rare cell identification and capture. Through compounding single-cell pipelines, we better reveal the heterogeneity within Comma-1D to identify subpopulations with specific functional characteristics.

Single-cell RNA-sequencing atlas of bovine caudal intervertebral discs: Discovery of heterogeneous cell populations with distinct roles in homeostasis.

Back and neck pain are significant healthcare burdens that are commonly associated with pathologies of the intervertebral disc (IVD). The poor understanding of the cellular heterogeneity within the IVD makes it difficult to develop regenerative IVD therapies. To address this gap, we developed an atlas of bovine (Bos taurus) caudal IVDs using single-cell RNA-sequencing (scRNA-seq). Unsupervised clustering resolved 15 unique clusters, which we grouped into the following annotated partitions: nucleus pulposus (NP), outer annulus fibrosus (oAF), inner AF (iAF), notochord, muscle, endothelial, and immune cells. Analyzing the pooled gene expression profiles of the NP, oAF, and iAF partitions allowed us to identify novel markers for NP (CP, S100B, H2AC18, SNORC, CRELD2, PDIA4, DNAJC3, CHCHD7, and RCN2), oAF (IGFBP6, CTSK, LGALS1, and CCN3), and iAF (MGP, COMP, SPP1, GSN, SOD2, DCN, FN1, TIMP3, WDR73, and GAL) cells. Network analysis on subpopulations of NP and oAF cells determined that clusters NP1, NP2, NP4, and oAF1 displayed gene expression profiles consistent with cell survival, suggesting these clusters may uniquely support viability under the physiological stresses of the IVD. Clusters NP3, NP5, oAF2, and oAF3 expressed various extracellular matrix (ECM)-associated genes, suggesting their role in maintaining IVD structure. Lastly, transcriptional entropy and pseudotime analyses found that clusters NP3 and NP1 had the most stem-like gene expression signatures of the NP partition, implying these clusters may contain IVD progenitor cells. Overall, results highlight cell type diversity within the IVD, and these novel cell phenotypes may enhance our understanding of IVD development, homeostasis, degeneration, and regeneration.

Cost Analysis of Noninvasive Blood-Based MicroRNA Testing Versus CT Scans for Follow-up in Patients With Testicular Germ-Cell Tumors.

BACKGROUND: Our group has developed a noninvasive blood-based microRNA (miRNA) test for improving diagnosis, disease monitoring, and relapse detection in malignant testicular germ-cell tumors (TGCTs). Performance analysis suggests the test is likely to have comparable sensitivity and specificity in detecting TGCT as computed tomography (CT), thus reducing the need for serial CT scans for follow-up monitoring, with associated reductions in cumulative radiation burden and second cancer risk. To facilitate clinical adoption, we undertook a cost analysis to identify the budget impact of replacing CT scans with miRNA testing within health care systems. METHODS: The TGCT aftercare pathway was mapped out using National Comprehensive Cancer Network guidelines. A Markov model was built to simulate the impact of the miRNA test on TGCT aftercare costs. Incidence, treatment probabilities, relapse rate, and death rate data were collected from published studies to populate the model. RESULTS: Applying our model to the US health care system, the miRNA test has the potential to save up to $69 million per year in aftercare expenses related to TGCT treatment, with exact savings depending on the adoption rate and test price. CONCLUSION: This analysis demonstrates the potential positive budget impact of adopting miRNA testing in place of CT scans in the clinical management of TGCTs.

Global gray and white matter metabolic changes after simian immunodeficiency virus infection in CD8-depleted rhesus macaques: proton MRS imaging at 3 T.

To test the hypotheses that global decreased neuro-axonal integrity reflected by decreased N-acetylaspartate (NAA) and increased glial activation reflected by an elevation in its marker, the myo-inositol (mI), present in a CD8-depleted rhesus macaque model of HIV-associated neurocognitive disorders. To this end, we performed quantitative MRI and 16 × 16 × 4 multivoxel proton MRS imaging (TE/TR = 33/1400 ms) in five macaques pre- and 4-6 weeks post-simian immunodeficiency virus infection. Absolute NAA, creatine, choline (Cho), and mI concentrations, gray and white matter (GM and WM) and cerebrospinal fluid fractions were obtained. Global GM and WM concentrations were estimated from 224 voxels (at 0.125 cm(3) spatial resolution over ~35% of the brain) using linear regression. Pre- to post-infection global WM NAA declined 8%: 6.6 ± 0.4 to 6.0 ± 0.5 mM (p = 0.05); GM Cho declined 20%: 1.3 ± 0.2 to 1.0 ± 0.1 mM (p < 0.003); global mI increased 11%: 5.7 ± 0.4 to 6.5 ± 0.5 mM (p < 0.03). Global GM and WM brain volume fraction changes were statistically insignificant. These metabolic changes are consistent with global WM (axonal) injury and glial activation, and suggest a possible GM host immune response.

A BAC-based transgenic mouse specifically expresses an inducible Cre in the urothelium.

Cre-loxp mediated conditional knockout strategy has played critical roles for revealing functions of many genes essential for development, as well as the causal relationships between gene mutations and diseases in the postnatal adult mice. One key factor of this strategy is the availability of mice with tissue- or cell type-specific Cre expression. However, the success of the traditional molecular cloning approach to generate mice with tissue specific Cre expression often depends on luck. Here we provide a better alternative by using bacterial artificial chromosome (BAC)-based recombineering to insert iCreERT2 cDNA at the ATG start of the Upk2 gene. The BAC-based transgenic mice express the inducible Cre specifically in the urothelium as demonstrated by mRNA expression and staining for LacZ expression after crossing with a Rosa26 reporter mouse. Taking into consideration the size of the gene of interest and neighboring genes included in a BAC, this method should be widely applicable for generation of mice with tissue specific gene expression or deletions in a more specific manner than previously reported.