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Cancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with it the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells. Leukaemic stem cells drive acute myeloid leukaemia (AML) progression and relapse but they are incompletely characterized. Here, the authors combine single-cell transcriptomics and clonal tracking using nuclear and mitochondrial somatic variants to distinguish healthy, pre-leukaemic and leukaemic stem cells in AML.

The transcriptome contains rich information on molecular, cellular and organismal phenotypes. However, experimental and statistical limitations constrain sensitivity and throughput of genetic screening with single-cell transcriptomics readout. To overcome these limitations, we introduce targeted Perturb-seq (TAP-seq), a sensitive, inexpensive and platform-independent method focusing single-cell RNA-seq coverage on genes of interest, thereby increasing the sensitivity and scale of genetic screens by orders of magnitude. TAP-seq permits routine analysis of thousands of CRISPR-mediated perturbations within a single experiment, detects weak effects and lowly expressed genes, and decreases sequencing requirements by up to 50-fold. We apply TAP-seq to generate perturbation-based enhancer–target gene maps for 1,778 enhancers within 2.5% of the human genome. We thereby show that enhancer–target association is jointly determined by three-dimensional contact frequency and epigenetic states, allowing accurate prediction of enhancer targets throughout the genome. In addition, we demonstrate that TAP-seq can identify cell subtypes with only 100 sequencing reads per cell. Targeted sequencing of perturbation effects offers a sensitive approach to capture genes of interest in CRISPR-mediated screens, enabling genome-scale screens at higher scale and lower cost than whole-transcriptome Perturb-seq.

Single-cell genomics technology has transformed our understanding of complex cellular systems. However, excessive cost and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high-content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all main hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the functional capacities of precisely mapped cell states to be measured at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry. Haas, Velten and colleagues use single-cell multiomics of human blood and bone marrow to generate a reference map allowing the quantitative linking of cytometry and proteo-genomic information.

Single-cell genomics has transformed our understanding of complex cellular systems. However, excessive costs and a lack of strategies for the purification of newly identified cell types impede their functional characterization and large-scale profiling. Here, we have generated high content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all hematopoietic cell types in healthy aging and leukemia. These reference maps enable the automatic design of cost-effective high-throughput cytometry schemes that outperform state-of-the-art approaches, accurately reflect complex topologies of cellular systems, and permit the purification of precisely defined cell states. The systematic integration of cytometry and proteo-genomic data enables the interpretation of functional capacities of such precisely mapped cell states at the single-cell level. Our study serves as an accessible resource and paves the way for a data-driven era in cytometry. Competing Interest Statement The oligo-coupled antibodies used in this study were a gift from BD Biosciences. Besides this, the authors declare no relevant conflicts of interest. Footnotes * Dataset has been updated to include a broader representation of AML patients (now, total of 15 patients). A reference-based analysis strategy for AML patient data is introduced. Figure 4 has been completely revised accordingly. More specific analyses on functional potential were added to figure 3, 7 and the supplement. * https://abseqapp.shiny.embl.de * https://figshare.com/projects/Single-cell_proteo-genomic_reference_maps_of_the_human_hematopoietic_system/94469

Abstract Cancer stem cells drive disease progression and relapse in many types of cancer. Despite this, a thorough characterization of these cells remains elusive and with i the ability to eradicate cancer at its source. In acute myeloid leukemia (AML), leukemic stem cells (LSCs) underlie mortality but are difficult to isolate due to their low abundance and high similarity to healthy hematopoietic stem cells (HSCs). Here, we demonstrate that LSCs, HSCs, and pre-leukemic stem cells can be identified and molecularly profiled by combining single-cell transcriptomics with lineage tracing using both nuclear and mitochondrial somatic variants. While mutational status discriminates between healthy and cancerous cells, gene expression distinguishes stem cells and progenitor cell populations. Our approach enables the identification of LSC-specific gene expression programs and the characterization of differentiation blocks induced by leukemic mutations. Taken together, we demonstrate the power of single-cell multi-omic approaches in characterizing cancer stem cells. Competing Interest Statement LMS is co-founder of Sophia Genetics and Levitas Bio and consultant for several companies on genetic analysis. Footnotes * Compared to the first manuscript version, data from 3 more patients was added and clonal tracking was refined using mitochondrial variants. * https://doi.org/10.6084/m9.figshare.12382685.v1

Effective environmental policies for the tropics depend on accurate, representative scientific data. However, there is strong evidence from particular disciplines and regions that existing research is patchily distributed. Here, we show that poor representation of sampling and citation in some biomes and across key environmental gradients from all disciplines for the entire tropics may lead to flawed scientific paradigms and inappropriate policy prescriptions. We map sampling locations and citations from 2 738 published studies in natural terrestrial tropical environments across all disciplines to identify gaps in field sampling effort and research attention. Five ecoregions – all in moist broadleaf forests – generate 22% of the total citations but cover only 3% of the tropical land area. By contrast, drier biomes with low tree cover account collectively for 57% of the tropical area but generate only 20% of total citations. Locations that are drier, colder, with greater plant species richness, lower tree cover and facing greater climate change extremes are under-sampled and under-cited. Our results will help to correct these imbalances to improve the scientific basis for environmental policies across the tropics. Research in the tropics is unevenly distributed across regions and biomes. Here, the authors find that moist broadleaf forests account for 73% of all tropical citations but cover 29% of the land area, while drier, climate-vulnerable areas with fewer trees remain under-sampled and under-cited.

A choledochal cyst is a relatively rare congenital anomaly of the biliary tree requiring surgery as the definitive treatment. Amongst the five Todani variants, type I poses a diagnostic and treatment challenge owing to its infrequent, yet clinically significant mimicry for type IVA cysts. Such an encounter, although rare, can significantly alter the course of management. We recommend extrahepatic cyst excision with biliary reconstruction as the standard treatment when preoperative and intraoperative imaging studies fall short in differentiating the aforementioned variants.

Little is known about the breadth of health care networks or the degree to which different insurers' networks overlap. To quantify network breadth and exclusivity (ie, overlap) among primary care physician (PCP), cardiology, and general acute care hospital networks for employer-based (large group and small group), individually purchased (marketplace), Medicare Advantage (MA), and Medicaid managed care (MMC) plans. This cross-sectional study included 1192 networks from Vericred. The analytic unit was the network-zip code-clinician type-market, which captured attributes of networks from the perspective of a hypothetical patient seeking access to in-network clinicians or hospitals within a 60-minute drive. Enrollment in a private insurance plan. Percentage of in-network physicians and/or hospitals within a 60-minute drive from a hypothetical patient in a given zip code (breadth). Number of physicians and/or hospitals within each network that overlapped with other insurers' networks, expressed as a percentage of the total possible number of shared connections (exclusivity). Descriptive statistics (mean, quantiles) were produced overall and by network breadth category, as follows: extra-small (<10%), small (10%-25%), medium (25%-40%), large (40%-60%), and extra-large (>60%). Networks were analyzed by insurance type, state, and insurance, physician, and/or hospital market concentration level, as measured by the Hirschman-Herfindahl index. Across all US zip code-network observations, 415 549 of 511 143 large-group PCP networks (81%) were large or extra-large compared with 138 485 of 202 702 MA (68%), 191 918 of 318 082 small-group (60%), 60 425 of 149 841 marketplace (40%), and 21 781 of 66 370 MMC (40%) networks. Large-group employer networks had broader coverage than all other network plans (mean [SD] PCP breadth: large-group employer-based plans, 57.3% [20.1]; small-group employer-based plans, 45.7% [21.4]; marketplace, 36,4% [21.2]; MMC, 32.3% [19.3]; MA, 47.4% [18.3]). MMC networks were the least exclusive (a mean [SD] overlap of 61.3% [10.5] for PCPs, 66.5% [9.8] for cardiology, and 60.2% [12.3] for hospitals). Networks were narrowest (mean [SD] breadth 42.4% [16.9]) and most exclusive (mean [SD] overlap 47.7% [23.0]) in California and broadest (79.9% [16.6]) and least exclusive (71.1% [14.6]) in Nebraska. Rising levels of insurer and market concentration were associated with broader and less exclusive networks. Markets with concentrated primary care and insurance markets had the broadest (median [interquartile range {IQR}], 75.0% [60.0%-83.1%]) and least exclusive (median [IQR], 63.7% [52.4%-73.7%]) primary care networks among large-group commercial plans, while markets with least concentration had the narrowest (median [IQR], 54.6% [46.8%-67.6%]) and most exclusive (median [IQR], 49.4% [41.9%-56.9%]) networks. In this study, narrower health care networks had a relatively large degree of overlap with other networks in the same geographic area, while broader networks were associated with physician, hospital, and insurance market concentration. These results suggest that many patients could switch to a lower-cost, narrow network plan without losing in-network access to their PCP, although future research is needed to assess the implications for care quality and clinical integration across in-network health care professionals and facilities in narrow network plans.

This cross-sectional study documents 2021 insurance network participation rates among hospital-based physicians nationally and by state.

The aim of this study was to analyze the influence of anthropometric variables, body composition variables and fat distribution on the postural control of sedentary Spanish males. 39 males aged between 25 and 60 years old, with a body mass index between 18 and 35 kg/m2, a stable body weight (no weight gain or loss of 2 kg or more in the last 3 months), and a level of physical activity classified as sedentary or low active (PAL <1.6 via accelerometer) were included in the study. Anthropometric variables (weight, height, body mass index and waist and hip perimeters), body composition variables (fat mass, lean mass and bone mass), body mass distribution (legs, android and total) and postural control were evaluated. A correlation was found between most of the anthropometric and body composition variables, assessed via the Somatosensory ratio of the Sensory Organization Test. Furthermore, individuals with a low percentage of leg and android fat mass presented improved scores when compared to those with higher percentages (97.05±2.66 vs. 95.84±1.64 and 97.00±2.61vs 95.83±1.69, respectively; p<0.05). Sedentary males with a greater body mass index and a higher percentage of leg fat mass and android fat mass are more proprioceptively challenged for maintaining balance. El objetivo de este estudio fue analizar la influencia de las variables antropométricas, de composición corporal y de distribución de la grasa en el control postural de varones españoles sedentarios. Se incluyeron en el estudio 39 varones de entre 25 y 60 años, con un índice de masa corporal entre 18 y 35 kg/m2, un peso corporal estable (sin ganancia o pérdida de peso igual o superior a 2 kg en los últimos 3 meses) y un nivel de actividad física clasificado como sedentario o poco activo (PAL <1,6 mediante acelerómetro). Se evaluaron variables antropométricas (peso, altura, índice de masa corporal y perímetros de cintura y cadera), variables de composición corporal (masa grasa, masa magra y masa ósea), distribución de la masa corporal (piernas, androide y total) y control postural. Se encontró una correlación entre la mayoría de las variables antropométricas y de composición corporal, evaluadas a través de la ratio Somatosensorial del Test de Organización Sensorial. Además, los individuos con un bajo porcentaje de masa grasa en piernas y androides presentaron mejores puntuaciones en comparación con aquellos con porcentajes más elevados (97,05±2,66 vs. 95,84±1,64 y 97,00±2,61vs 95,83±1,69, respectivamente; p<0,05). Los varones sedentarios con un mayor índice de masa corporal y un mayor porcentaje de masa grasa en las piernas y masa grasa androide tienen más dificultades propioceptivas para mantener el equilibrio.