Diego Jaitin

Bio: Diego Jaitin is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Chromatin & Immune system. The author has an hindex of 26, co-authored 45 publications receiving 7394 citations.

Host microbiota constantly control maturation and function of microglia in the CNS

Abstract: As the tissue macrophages of the CNS, microglia are critically involved in diseases of the CNS. However, it remains unknown what controls their maturation and activation under homeostatic conditions. We observed substantial contributions of the host microbiota to microglia homeostasis, as germ-free (GF) mice displayed global defects in microglia with altered cell proportions and an immature phenotype, leading to impaired innate immune responses. Temporal eradication of host microbiota severely changed microglia properties. Limited microbiota complexity also resulted in defective microglia. In contrast, recolonization with a complex microbiota partially restored microglia features. We determined that short-chain fatty acids (SCFA), microbiota-derived bacterial fermentation products, regulated microglia homeostasis. Accordingly, mice deficient for the SCFA receptor FFAR2 mirrored microglia defects found under GF conditions. These findings suggest that host bacteria vitally regulate microglia maturation and function, whereas microglia impairment can be rectified to some extent by complex microbiota.

Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types

Abstract: In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues.

m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation

Abstract: Naive and primed pluripotent states retain distinct molecular properties, yet limited knowledge exists on how their state transitions are regulated. Here, we identify Mettl3, an N(6)-methyladenosine (m(6)A) transferase, as a regulator for terminating murine naive pluripotency. Mettl3 knockout preimplantation epiblasts and naive embryonic stem cells are depleted for m(6)A in mRNAs, yet are viable. However, they fail to adequately terminate their naive state and, subsequently, undergo aberrant and restricted lineage priming at the postimplantation stage, which leads to early embryonic lethality. m(6)A predominantly and directly reduces mRNA stability, including that of key naive pluripotency-promoting transcripts. This study highlights a critical role for an mRNA epigenetic modification in vivo and identifies regulatory modules that functionally influence naive and primed pluripotency in an opposing manner.

Chromatin state dynamics during blood formation

Abstract: Chromatin modifications are crucial for development, yet little is known about their dynamics during differentiation. Hematopoiesis provides a well-defined model to study chromatin state dynamics; however, technical limitations impede profiling of homogeneous differentiation intermediates. We developed a high-sensitivity indexing-first chromatin immunoprecipitation approach to profile the dynamics of four chromatin modifications across 16 stages of hematopoietic differentiation. We identify 48,415 enhancer regions and characterize their dynamics. We find that lineage commitment involves de novo establishment of 17,035 lineage-specific enhancers. These enhancer repertoire expansions foreshadow transcriptional programs in differentiated cells. Combining our enhancer catalog with gene expression profiles, we elucidate the transcription factor network controlling chromatin dynamics and lineage specification in hematopoiesis. Together, our results provide a comprehensive model of chromatin dynamics during development.

Massively parallel digital transcriptional profiling of single cells

Abstract: Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system’s technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system’s ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients. Single-cell gene expression analysis is challenging. This work describes a new droplet-based single cell RNA-seq platform capable of processing tens of thousands of cells across 8 independent samples in minutes, and demonstrates cellular subtypes and host–donor chimerism in transplant patients.

Spatial reconstruction of single-cell gene expression data

Abstract: Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.