Obtention of viable cell suspensions from breast cancer tumor biopsies for 3D chromatin conformation and single-cell transcriptome analysis.

Molecular and cellular characterization of tumors is essential due to the complex and heterogeneous nature of cancer. In recent decades, many bioinformatic tools and experimental techniques have been developed to achieve personalized characterization of tumors. However, sample handling continues to be a major challenge as limitations such as prior treatments before sample acquisition, the amount of tissue obtained, transportation, or the inability to process fresh samples pose a hurdle for experimental strategies that require viable cell suspensions. Here, we present an optimized protocol that allows the recovery of highly viable cell suspensions from breast cancer primary tumor biopsies. Using these cell suspensions we have successfully characterized genome architecture through Hi-C. Also, we have evaluated single-cell gene expression and the tumor cellular microenvironment through single-cell RNAseq. Both technologies are key in the detailed and personalized molecular characterization of tumor samples. The protocol described here is a cost-effective alternative to obtain viable cell suspensions from biopsies simply and efficiently.

Single-cell RNA sequencing and cell-cell communication analysis reveal tumor microenvironment associated with chemotherapy responsiveness in ovarian cancer.

BACKGROUND: To investigate the impact of the tumor microenvironment (TME) on the responsiveness to chemotherapy in ovarian cancer (OV). METHODS: We integrated single cell RNA-seq datasets of OV containing chemo-response information, and characterize their clusters based on different TME sections. We focus on analyzing cell-cell communication to elaborate on the mechanisms by which different components of the TME directly influence the chemo-response of tumor cells. RESULTS: scRNA-seq datasets were annotated according to specific markers for different cell types. Differential analysis of malignant epithelial cells revealed that chemoresistance was associated with the TME. Notably, distinct TME components exhibited varying effects on chemoresistance. Enriched SPP1+ tumor-associated macrophages in chemo-resistant patients could promote chemoresistance through SPP1 binding to CD44 on tumor cells. Additionally, the overexpression of THBS2 in stromal cells could promote chemoresistance through binding with CD47 on tumor cells. In contrast, GZMA in the lymphocytes could downregulate the expression of PARD3 through direct interaction with PARD3, thereby attenuating chemoresistance in tumor cells. CONCLUSION: Our study indicates that the non-tumor cell components of the TME (e.g. SPP1+ TAMs, stromal cells and lymphocytes) can directly impact the chemo-response of OV and targeting the TME was potentially crucial in chemotherapy of OV.

Unraveling the metastatic niche in breast cancer bone metastasis through single-cell RNA sequencing.

PURPOSE: Breast cancer (BRCA) is characterized by a unique metastatic pattern, often presenting with bone metastasis (BoM), posing significant clinical challenges. Through the study of the immune microenvironment in BRCA BoM offer perspectives for therapeutic interventions targeting this specific metastatic manifestation of BRCA. METHODS: This study employs single-cell RNA sequencing and TCGA data analysis to comprehensively compare primary tumors (PT), lymph node metastasis (LN), and BoM. RESULTS AND CONCLUSIONS: Our investigation identifies a metastatic niche in BoM marked by an increased abundance of cancer-associated fibroblasts (CAFs) and reduced immune cell presence. A distinct subtype (State 1) of BRCA BoM cells associated with adverse prognosis is identified. State 1, displaying heightened stemness traits, may represent an initiation phase for BoM in BRCA. Complex cell communications involving tumor, stromal, and immune cells are revealed. Interactions of FN1, SPP1, and MDK correlate with elevated immune cells in BoM. CD46, MDK, and PTN interactions drive myofibroblast activation and proliferation, contributing to tissue remodeling. Additionally, MDK, PTN, and FN1 interactions influence FAP+ CAF activation, impacting cell adhesion and migration in BoM. These insights deepen our understanding of the metastatic niche in breast cancer BoM.

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

BACKGROUND: Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS: A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS: We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Upregulated PPP1R14B is connected to cancer progression and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an oncogenic gene found in a variety of tumors, but its role in the prognosis and development of kidney renal clear cell carcinoma (KIRC) remains unknown. Our study aimed to determine whether PPP1R14B could be a prognostic biomarker for KIRC and its role in the development of KIRC. METHODS: In this work, we used The Cancer Genome Atlas (TCGA) database to explore the expression of PPP1R14B in tumor tissues, its relationship with the prognosis of tumor patients, and its role in tumor occurrence and development. We validated our findings using the International Cancer Genome Consortium (ICGC) cohort, our clinical samples, and in vitro experiments. RESULTS: PPP1R14B was upregulated in KIRC compared to adjacent normal tissue. Moreover, multivariate analysis revealed that upregulated PPP1R14B expression was an independent risk factor for KIRC progression. High-PPP1R14B groups had shorter overall survival (OS) and disease-free survival (DFS) in TCGA and ICGC cohorts. We used Cell Counting Kit-8 (CCK8) and scratch wound healing assay to explore the proliferation and migration of KIRC cells following PPP1R14B knockdown. Our results indicated that PPP1R14B knockdown significantly reduced the proliferation and migration of KIRC cells in vitro. We also explored the possible cellular mechanisms of PPP1R14B through the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) analysis, and TISIDB analysis. The function enrich analysis revealed that PPP1R14B-related genes were mainly enriched in purine metabolism and the macromolecule catabolic process. PPP1R14B expression was associated with tumor-infiltrating immune cells (TIICs) in the TCGA cohort, and the results of single-cell RNA-seq (scRNA) further demonstrated that PPP1R14B expression was associated with the enhanced infiltration of CD8 + T lymphocytes. CONCLUSION: PPP1R14B may serve as a prognostic biomarker in KIRC, affect purine metabolism, activate immune infiltration, and promote KIRC cell migration.

Single-cell RNA sequencing reveals the lineage of malignant epithelial cells and upregulation of TAGLN2 promotes peritoneal metastasis in gastric cancer.

BACKGROUND: Peritoneal metastasis (PM) is an important factor contributing to poor prognosis in patients with gastric cancer (GC). Transcriptomic sequencing has been used to explore the molecular changes in metastatic cancers, but comparing the bulk RNA-sequencing data between primary tumors and metastases in PM studies is unreasonable due to the small proportion of tumor cells in PM tissues. METHODS: We performed single-cell RNA-sequencing analysis on four gastric adenocarcinoma specimens, including one primary tumor sample (PT), one adjacent nontumoral sample (PN), one peritoneal metastatic sample (MT) and one normal peritoneum sample (MN), from the same patient. Pseudotime trajectory analysis was used to display the process by which nonmalignant epithelial cells transform into tumor cells and then metastasize to the peritoneum. Finally, in vitro and in vivo assays were used to validate one of the selected genes that promote peritoneal metastasis. RESULTS: Single-cell RNA sequencing showed that a development curve was found from normal mucosa to tumor tissues and then into metastatic sites on peritoneum. TAGLN2 was found to trigger this metastasis process. The migration and invasion capability of GC cells were changed by downregulating and upregulating TAGLN2 expression. Mechanistically, TAGLN2 might modulate tumor metastasis via alterations in cell morphology and several signaling pathways, thus promoting epithelial-mesenchymal transition (EMT). CONCLUSIONS: In summary, we identified and validated TAGLN2 as a novel gene involved in GC peritoneal metastasis. This study provided valuable insight into the mechanisms of GC metastasis and developed a potential therapeutic target to prevent GC cell dissemination.

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19.

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

Pulmonary Mesenchymal Stem Cells in Mild Cases of COVID-19 Are Dedicated to Proliferation; In Severe Cases, They Control Inflammation, Make Cell Dispersion, and Tissue Regeneration.

Mesenchymal stem cells (MSCs) are multipotent adult stem cells present in virtually all tissues; they have potent self-renewal capacity and differentiate into multiple cell types. For many reasons, these cells are a promising therapeutic alternative to treat patients with severe COVID-19 and pulmonary post-COVID sequelae. These cells are not only essential for tissue regeneration; they can also alter the pulmonary environment through the paracrine secretion of several mediators. They can control or promote inflammation, induce other stem cells differentiation, restrain the virus load, and much more. In this work, we performed single-cell RNA-seq data analysis of MSCs in bronchoalveolar lavage samples from control individuals and COVID-19 patients with mild and severe clinical conditions. When we compared samples from mild cases with control individuals, most genes transcriptionally upregulated in COVID-19 were involved in cell proliferation. However, a new set of genes with distinct biological functions was upregulated when we compared severely affected with mild COVID-19 patients. In this analysis, the cells upregulated genes related to cell dispersion/migration and induced the γ-activated sequence (GAS) genes, probably triggered by IFNGR1 and IFNGR2. Then, IRF-1 was upregulated, one of the GAS target genes, leading to the interferon-stimulated response (ISR) and the overexpression of many signature target genes. The MSCs also upregulated genes involved in the mesenchymal-epithelial transition, virus control, cell chemotaxis, and used the cytoplasmic RNA danger sensors RIG-1, MDA5, and PKR. In a non-comparative analysis, we observed that MSCs from severe cases do not express many NF-κB upstream receptors, such as Toll-like (TLRs) TLR-3, -7, and -8; tumor necrosis factor (TNFR1 or TNFR2), RANK, CD40, and IL-1R1. Indeed, many NF-κB inhibitors were upregulated, including PPP2CB, OPTN, NFKBIA, and FHL2, suggesting that MSCs do not play a role in the "cytokine storm" observed. Therefore, lung MSCs in COVID-19 sense immune danger and act protectively in concert with the pulmonary environment, confirming their therapeutic potential in cell-based therapy for COVID-19. The transcription of MSCs senescence markers is discussed.

MYC Promotes Bone Marrow Stem Cell Dysfunction in Fanconi Anemia.

Bone marrow failure (BMF) in Fanconi anemia (FA) patients results from dysfunctional hematopoietic stem and progenitor cells (HSPCs). To identify determinants of BMF, we performed single-cell transcriptome profiling of primary HSPCs from FA patients. In addition to overexpression of p53 and TGF-ß pathway genes, we identified high levels of MYC expression. We correspondingly observed coexistence of distinct HSPC subpopulations expressing high levels of TP53 or MYC in FA bone marrow (BM). Inhibiting MYC expression with the BET bromodomain inhibitor (+)-JQ1 reduced the clonogenic potential of FA patient HSPCs but rescued physiological and genotoxic stress in HSPCs from FA mice, showing that MYC promotes proliferation while increasing DNA damage. MYC-high HSPCs showed significant downregulation of cell adhesion genes, consistent with enhanced egress of FA HSPCs from bone marrow to peripheral blood. We speculate that MYC overexpression impairs HSPC function in FA patients and contributes to exhaustion in FA bone marrow.

Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq.

PURPOSE: Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer. METHODS: Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types. RESULTS: Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues. CONCLUSION: Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior.

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

Prediction of cell position using single-cell transcriptomic data: an iterative procedure.

Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.