Secretion encoded single-cell sequencing (SEC-seq) uncovers gene expression signatures associated with high VEGF-A secretion in mesenchymal stromal cells

Functional variation among mesenchymal stem cells derived from different tissue sources

Background

Mesenchymal stem cells (MSCs) are increasingly recognized for their regenerative potential. However, their clinical application is hindered by their inherent variability, which is influenced by various factors, such as the tissue source, culture conditions, and passage number.

Methods

MSCs were sourced from clinically relevant tissues, including adipose tissue-derived MSCs (ADMSCs, n = 2), chorionic villi-derived MSCs (CMMSCs, n = 2), amniotic membrane-derived MSCs (AMMSCs, n = 3), and umbilical cord-derived MSCs (UCMSCs, n = 3). Passages included the umbilical cord at P0 (UCMSCP0, n = 2), P3 (UCMSCP3, n = 2), and P5 (UCMSCP5, n = 2) as well as the umbilical cord at P5 cultured under low-oxygen conditions (UCMSCP5L, n = 2).

Results

We observed that MSCs from different tissue origins clustered into six distinct functional subpopulations, each with varying proportions. Notably, ADMSCs exhibited a higher proportion of subpopulations associated with vascular regeneration, suggesting that they are beneficial for applications in vascular regeneration. Additionally, CMMSCs had a high proportion of subpopulations associated with reproductive processes. UCMSCP5 and UCMSCP5L had higher proportions of subpopulations related to female reproductive function than those for earlier passages. Furthermore, UCMSCP5L, cultured under low-oxygen (hypoxic) conditions, had a high proportion of subpopulations associated with pro-angiogenic characteristics, with implications for optimizing vascular regeneration.

Conclusions

This study revealed variation in the distribution of MSC subpopulations among different tissue sources, passages, and culture conditions, including differences in functions related to vascular and reproductive system regeneration. These findings hold promise for personalized regenerative medicine and may lead to more effective clinical treatments across a spectrum of medical conditions.

Optimizing cell therapy by sorting cells with high extracellular vesicle secretion

Critical challenges remain in clinical translation of extracellular vesicle (EV)-based therapeutics due to the absence of methods to enrich cells with high EV secretion. Current cell sorting methods are limited to surface markers that are uncorrelated to EV secretion or therapeutic potential. Here, we utilize a nanovial technology for enrichment of millions of single cells based on EV secretion. This approach is applied to select mesenchymal stem cells (MSCs) with high EV secretion as therapeutic cells for improving treatment. The selected MSCs exhibit distinct transcriptional profiles associated with EV biogenesis and vascular regeneration and maintain high levels of EV secretion after sorting and regrowth. In a mouse model of myocardial infarction, treatment with high-secreting MSCs improves heart functions compared to treatment with low-secreting MSCs. These findings highlight the therapeutic importance of EV secretion in regenerative cell therapies and suggest that selecting cells based on EV secretion could enhance therapeutic efficacy.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

SEC-seq: association of molecular signatures with antibody secretion in thousands of single human plasma cells

The secreted products of cells drive many functions in vivo; however, methods to link this functional information to surface markers and transcriptomes have been lacking. By accumulating secretions close to secreting cells held within cavity-containing hydrogel nanovials, we demonstrate workflows to analyze the amount of IgG secreted from single human B cells and link this information to surface markers and transcriptomes from the same cells. Measurements using flow cytometry and imaging flow cytometry corroborate the association between IgG secretion and CD38/CD138. By using oligonucleotide-labeled antibodies we find that upregulation of pathways for protein localization to the endoplasmic reticulum and mitochondrial oxidative phosphorylation are most associated with high IgG secretion, and uncover surrogate plasma cell surface markers (e.g., CD59) defined by the ability to secrete IgG. Altogether, this method links quantity of secretion with single-cell sequencing (SEC-seq) and enables researchers to fully explore the links between genome and function, laying the foundation for discoveries in immunology, stem cell biology, and beyond.

Optimizing Cell Therapy by Sorting Cells with High Extracellular Vesicle Secretion

Critical challenges remain in clinical translation of extracellular vesicle (EV)-based therapeutics due to the absence of methods to enrich cells with high EV secretion. Current cell sorting methods are limited to surface markers that are uncorrelated to EV secretion or therapeutic potential. We developed a nanovial technology for enrichment of millions of single cells based on EV secretion. This approach was applied to select mesenchymal stem cells (MSCs) with high EV secretion as therapeutic cells for improving treatment. The selected MSCs exhibited distinct transcriptional profiles associated with EV biogenesis and vascular regeneration and maintained high levels of EV secretion after sorting and regrowth. In a mouse model of myocardial infarction, treatment with high-secreting MSCs improved heart functions compared to treatment with low-secreting MSCs. These findings highlight the therapeutic importance of EV secretion in regenerative cell therapies and suggest that selecting cells based on EV secretion could enhance therapeutic efficacy.