SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing.

BACKGROUND: Single-cell RNA-sequencing is revolutionising the study of cellular and tissue-wide heterogeneity in a large number of biological scenarios, from highly tissue-specific studies of disease to human-wide cell atlases. A central task in single-cell RNA-sequencing analysis design is the calculation of cell type-specific genes in order to study the differential impact of different replicates (e.g. tumour vs. non-tumour environment) on the regulation of those genes and their associated networks. The crucial task is the efficient and reliable calculation of such cell type-specific 'marker' genes. These optimise the ability of the experiment to isolate highly-specific cell phenotypes of interest to the analyser. However, while methods exist that can calculate marker genes from single-cell RNA-sequencing, no such method places emphasise on specific cell phenotypes for downstream study in e.g. differential gene expression or other experimental protocols (spatial transcriptomics protocols for example). Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA-sequencing data which reliably characterise highly-specific and niche populations of cells in numerous different biological data-sets. RESULTS: SMaSH extracts robust and biologically well-motivated marker genes, which characterise a given single-cell RNA-sequencing data-set better than existing computational approaches for general marker gene calculation. We demonstrate the utility of SMaSH through its substantial performance improvement over several existing methods in the field. Furthermore, we evaluate the SMaSH markers on spatial transcriptomics data, demonstrating they identify highly localised compartments of the mouse cortex. CONCLUSION: SMaSH is a new methodology for calculating robust markers genes from large single-cell RNA-sequencing data-sets, and has implications for e.g. effective gene identification for probe design in downstream analyses spatial transcriptomics experiments. SMaSH has been fully-integrated with the ScanPy framework and provides a valuable bioinformatics tool for cell type characterisation and validation in every-growing data-sets spanning over 50 different cell types across hundreds of thousands of cells.

Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts.

BACKGROUND: Myelodysplastic syndromes are a diverse and common group of chronic hematologic cancers. The identification of new genetic lesions could facilitate new diagnostic and therapeutic strategies. METHODS: We used massively parallel sequencing technology to identify somatically acquired point mutations across all protein-coding exons in the genome in 9 patients with low-grade myelodysplasia. Targeted resequencing of the gene encoding RNA splicing factor 3B, subunit 1 (SF3B1), was also performed in a cohort of 2087 patients with myeloid or other cancers. RESULTS: We identified 64 point mutations in the 9 patients. Recurrent somatically acquired mutations were identified in SF3B1. Follow-up revealed SF3B1 mutations in 72 of 354 patients (20%) with myelodysplastic syndromes, with particularly high frequency among patients whose disease was characterized by ring sideroblasts (53 of 82 [65%]). The gene was also mutated in 1 to 5% of patients with a variety of other tumor types. The observed mutations were less deleterious than was expected on the basis of chance, suggesting that the mutated protein retains structural integrity with altered function. SF3B1 mutations were associated with down-regulation of key gene networks, including core mitochondrial pathways. Clinically, patients with SF3B1 mutations had fewer cytopenias and longer event-free survival than patients without SF3B1 mutations. CONCLUSIONS: Mutations in SF3B1 implicate abnormalities of messenger RNA splicing in the pathogenesis of myelodysplastic syndromes. (Funded by the Wellcome Trust and others.).

[Hypoglycemia in childhood. II. Clinical aspects and therapy of hypoglycemia in childhood]. / Hipoglikemija u decijem uzrastu. Deo II: Klinika i terapija hipoglikemija u decijem uzrastu.

Regardless of age and body weight, hypoglycemia is defined as the glycemia value below 2.6 mmol/l. This is the borderline glycemia value, below which the suffering begins which directly endangers the CNS development in the newborn period. The fact that we registered hypoglycemias in 2/3 of premature infants and in 3/4 of low birth weight infants at registration on the ward for premature infants at the Institute for Mother and Child Health Protection, in the course of 1988, tells of the complexity and seriousness of the situation with which we meet during the depopulation of the inhabitants. The paper gives physiological basics for the understanding of glycose homeostasis in the organism, as the most frequent forms of hypoglycemia with which the physician meets in practice. A diagnosing and hypoglycemia treatment algorithm is given.

[Hypoglycemia in childhood. I. Physiology, pathophysiology and the classification of hypoglycemia]. / Hipoglikemija u decijem uzrastu. I deo: Fiziologija, patofiziologija i podela hipoglikemija.

Regardless of age and body mass, hypoglycemia is defined as the glycemia value below 2.6 mmol/l. This is the borderline glycemia value, below which the suffering begins which directly endangers the CNS development in the newborn period. The fact that we registered hypoglycemias in 2/3 of premature infants and in 3/4 of low birth weight infants at registration on the ward for premature infants at the Institute for Mother and Child Health Protection, in the course of 1988, tells of the complexity and seriousness of the situation with which we meet during the depopulation of the inhabitants. The paper gives physiological basics for the understanding of glucose homeostasis in the organism, as the most frequent forms of hypoglycemia with which the physician meets in practice. A diagnosing and hypoglycemia treatment algorithm is given.