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Kossenkov, A.

Publications and source records attributed to Kossenkov, A..

2 recordsLinked to original sources

IL-27 receptor signaling regulated stress myelopoiesis drives Abdominal Aortic Aneurysm development

Abdominal Aortic Aneurysm (AAA) is a vascular disease, where aortic wall degradation is mediated by accumulated immune cells. Though cytokines regulate the inflammatory milieu within the aortic wall, their contribution to AAA through distant alterations, particularly in the control of hematopoietic stem cells proliferation and myeloid cell differentiation remains poorly defined. Here we report an unexpected pathogenic role for interleukin-27 receptor (IL-27R) in AAA development as genetic inactivation of IL-27R protected mice from AAA induced by Angiotensin (Ang) II. The mitigation of AAA in IL-27R deficient mice is associated with a blunted accumulation of myeloid cells in suprarenal aortas due to the surprising attenuation of Ang II-induced expansion of HSCs. The loss of IL-27R engages transcriptional programs that promote HSCs quiescence and suppresses myeloid lineage differentiation, decreasing mature cell production and myeloid cell accumulation in the aorta. We, therefore, illuminate how a prominent vascular disease can be distantly driven by cytokine dependent regulation of the bone marrow precursors.

immunology

Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues

The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.

genomics