PHF6 loss reduces leukemia stem cell activity in an acute myeloid leukemia mouse model.

Acute myeloid leukemia (AML) is a malignant hematologic disease caused by gene mutations and genomic rearrangements in hematologic progenitors. The PHF6 (PHD finger protein 6) gene is highly conserved and located on the X chromosome in humans and mice. We found that PHF6 was highly expressed in AML cells with MLL rearrangement and was related to the shortened survival time of AML patients. In our study, we knocked out the Phf6 gene at different disease stages in the AML mice model. Moreover, we knocked down PHF6 by shRNA in two AML cell lines and examined the cell growth, apoptosis, and cell cycle. We found that PHF6 deletion significantly inhibited the proliferation of leukemic cells and prolonged the survival time of AML mice. Interestingly, the deletion of PHF6 at a later stage of the disease displayed a better anti-leukemia effect. The expressions of genes related to cell differentiation were increased, while genes that inhibit cell differentiation were decreased with PHF6 knockout. It is very important to analyze the maintenance role of PHF6 in AML, which is different from its tumor-suppressing function in T-cell acute lymphoblastic leukemia (T-ALL). Our study showed that inhibiting PHF6 expression may be a potential therapeutic strategy targeting AML patients.

SCAR: Single-cell and Spatially-resolved Cancer Resources.

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

SCAN: Spatiotemporal Cloud Atlas for Neural cells.

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.

It is a challenge to efficiently integrate and present the tremendous amounts of single-cell data generated from multiple tissues of various species. Here, we create a new database named SPEED for single-cell pan-species atlas in the light of ecology and evolution for development and diseases (freely accessible at http://8.142.154.29 or http://speedatlas.net). SPEED is an online platform with 4 data modules, 7 function modules and 2 display modules. The 'Pan' module is applied for the interactive analysis of single cell sequencing datasets from 127 species, and the 'Evo', 'Devo', and 'Diz' modules provide comprehensive analysis of single-cell atlases on 18 evolution datasets, 28 development datasets, and 85 disease datasets. The 'C2C', 'G2G' and 'S2S' modules explore intercellular communications, genetic regulatory networks, and cross-species molecular evolution. The 'sSearch', 'sMarker', 'sUp', and 'sDown' modules allow users to retrieve specific data information, obtain common marker genes for cell types, freely upload, and download single-cell datasets, respectively. Two display modules ('HOME' and 'HELP') offer easier access to the SPEED database with informative statistics and detailed guidelines. All in all, SPEED is an integrated platform for single-cell RNA sequencing (scRNA-seq) and single-cell whole-genome sequencing (scWGS) datasets to assist the deep-mining and understanding of heterogeneity among cells, tissues, and species at multi-levels, angles, and orientations, as well as provide new insights into molecular mechanisms of biological development and pathogenesis.

Identification of lncRNAs and Genes Responsible for Fatness and Fatty Acid Composition Traits between the Tibetan and Yorkshire Pigs.

Tibetan pigs from the Tibetan Plateau are characterized with a significant phenotypic difference relative to lowland pigs. In this study, a significant difference of the fatness and fatty acid composition traits was observed between the Tibetan and Yorkshire pigs. To uncover the involved mechanism, the expression profile of long noncoding RNAs (lncRNAs) and genes was compared between them. After serial filtered steps, 1,964 lncRNAs were obtained through our computational pipeline. In total, 63 and 715 lncRNAs and genes were identified to be differentially expressed. Evidence from cis- and trans-targeting analysis of lncRNAs demonstrated that some lncRNAs, such as MSTRG.14097 and MSTRG.8034, played important roles in the fatness and fatty acid composition traits. Bioinformatics analysis revealed that many candidate genes were responsible for the two traits. Of these, FASN, ACACA, SCD, ME3, PDHB, ACSS1, ACSS2, and ACLY were identified, which functioned in regulating the level of hexadecanoic acid, hexadecenoic acid, octadecenoic acid, and monounsaturated fatty acid. And LPGAT1, PDK4, ACAA1, and ADIPOQ were associated with the content of stearic acid, octadecadienoic acid, and polyunsaturated fatty acid. Candidate genes, which were responsible for fatness trait, consisted of FGF2, PLAG1, ADIPOQ, IRX3, MIF, IL-34, ADAM8, HMOX1, Vav1, and TLR8. In addition, association analysis also revealed that 34 and 57 genes significantly correlated to the fatness and fatty acid composition trait, respectively. Working out the mechanism caused by these lncRNAs and candidate genes is proven to be complicated but is invaluable to our understanding of fatness and fatty acid composition traits.