Search

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

Blood circulates throughout the human body and contains molecules drawn from virtually every tissue, including the microbes and viruses which colonize the body. Through massive shotgun sequencing of circulating cell-free DNA from the blood, we identified hundreds of new bacteria and viruses which represent previously unidentified members of the human microbiome. Analyzing cumulative sequence data from 1,351 blood samples collected from 188 patients enabled us to assemble 7,190 contiguous regions (contigs) larger than 1 kbp, of which 3,761 are novel with little or no sequence homology in any existing databases. The vast majority of these novel contigs possess coding sequences, and we have validated their existence both by finding their presence in independent experiments and by performing direct PCR amplification. When their nearest neighbors are located in the tree of life, many of the organisms represent entirely novel taxa, showing that microbial diversity within the human body is substantially broader than previously appreciated.

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput. [ABSTRACT FROM AUTHOR]

Reliance on rodents for understanding pancreatic genetics, development and islet function could limit progress in developing interventions for human diseases such as diabetes mellitus. Similarities of pancreas morphology and function suggest that porcine and human pancreas developmental biology may have useful homologies. However, little is known about pig pancreas development. To fill this knowledge gap, we investigated fetal and neonatal pig pancreas at multiple, crucial developmental stages using modern experimental approaches. Purification of islet β-, α- and δ-cells followed by transcriptome analysis (RNA-seq) and immunohistology identified celland stage-specific regulation, and revealed that pig and human islet cells share characteristic features that are not observed in mice. Morphometric analysis also revealed endocrine cell allocation and architectural similarities between pig and human islets. Our analysis unveiled scores of signaling pathways linked to native islet β-cell functional maturation, including evidence of fetal α-cell GLP-1 production and signaling to β-cells. Thus, the findings and resources detailed here show how pig pancreatic islet studies complement other systems for understanding the developmental programs that generate functional islet cells, and that are relevant to human pancreatic diseases. [ABSTRACT FROM AUTHOR]

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput., Competing Interests: Competing interestsThe author declares no competing interests., (© The Author(s) 2023.)