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Single‐cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single‐cell RNA‐seq data. We show that COMET outperforms other methods for the identification of single‐gene panels and enables, for the first time, prediction of multi‐gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single‐ and multi‐gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non‐parametric statistical framework and can be used as‐is on various high‐throughput datasets in addition to single‐cell RNA‐sequencing data. COMET is available for use via a web interface ( http://www.cometsc.com/ ) or a stand‐alone software package ( https://github.com/MSingerLab/COMETSC ). Synopsis COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest. COMET is a computational tool for combinatorial prediction of marker panels from single‐cell transcriptomic data. COMET's statistical framework enables controlling for specificity and sensitivity in predicted marker panels. Staining by flow‐cytometry validates that COMET identifies novel and favorable single‐ and multi‐gene marker panels for cellular subtypes. COMET is available via a web interface ( http://www.cometsc.com/ ) or downloadable software package ( https://github.com/MSingerLab/COMETSC ). Graphical Abstract COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest.

An effective genetic marker panel that can be used with noninvasive samples is useful for population genetics and the conservation management of endangered species. We aimed to develop a microsatellite marker panel for Tibetan macaques (Macaca thibetana), with good levels of polymorphism, stability, and repeatability and suitable for use with noninvasive samples. We designed 83 primer pairs to screen for polymorphic loci based on a tetranucleotide microsatellite dataset. We tested the loci using 16 tissue samples from Sichuan, Guangxi, and Anhui Province in China, and then 106 fecal samples from three wild populations (Huangshan: Anhui Province, Labahe Natural Reserve: Sichuan Province, Fanjingshan Natural Reserve: Guizhou Province). We used the resulting marker panel to identify individuals and estimate genetic diversity in the three populations. We found that 37 novel loci were polymorphic when we genotyped tissue samples. Fifteen of these loci were high polymorphic, sensitive and stable, they were suitable for fecal samples, and we could identify individuals effectively using a subset of six loci. Using these 6 loci, we identified 89 individuals from the 106 fecal samples. The three wild populations had relatively high genetic diversity, with polymorphism information content ranging from 0.530 to 0.678. The Huangshan population had the highest genetic diversity and the largest number of alleles, whereas genetic diversity was the lowest in the Fanjingshan population. The marker panel will facilitate future population genetic research on Tibetan macaques.

Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research, which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping‐in‐thousands (GTSeq) is one approach used to create standardized marker panels for nonmodel organisms. Here, we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (Sander vitreus) from the Great Lakes region of North America. High genome‐coverage sequencing conducted using RAD capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen, which were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave‐one‐out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false‐positive rates of parent‐offspring and full‐sibling kinship identification were found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region. New genetic marker panels are an important resource for wildlife management and conservation. Here, we develop and test a new GTSeq marker panel for walleye with Laurentian Great Lakes ancestry. The new panel provides accurate genetic stock and pedigree assignment for a large number of populations that will facilitate interjurisdictional research and collaboration.

Cancer-associated fibroblasts (CAFs) are important factors in the progression of hepatocellular carcinoma (HCC). But the characterization of these cells remains incomplete. This study aims to identify a panel of markers for CAFs that are associated with HCC progression. The sequencing data and clinicopathological characteristics of 366 patients were obtained from the Cancer Genome Atlas (TCGA) database (366 HCC tissues and there were 50/366 cases with corresponding normal liver tissues). In vitro validation of the markers was performed by quantitative real-time PCR using the hepatic stellate cell line LX2 induced by the HCC cell line Huh7. The activation of LX2 was confirmed by α-smooth muscle actin and fibroblast activation protein, using quantitative real-time PCR and immunofluorescence staining. In vivo detections of the 12 markers were done in 40 tissue samples (30 HCC and 10 normal). We successfully identified 12 CAF markers from TCGA data: FGF5, CXCL5, IGFL2, MMP1, ADAM32, ADAM18, IGFL1, FGF8, FGF17, FGF19, FGF4, and FGF23. The 12-marker panel was associated with the pathological and clinical progressions of HCC. All 12 markers were upregulated in vitro. In vivo expressions of these markers were paralleled with those in TCGA data. A 12-marker panel of CAFs in HCC is identified, which is associated with both pathological and clinical progressions of cancer.

Aims Patients with heart failure (HF) exhibit poor prognosis, which is further deteriorated by pulmonary hypertension (PH), with negative impact on morbidity and mortality. As PH due to left HF (LHF‐PH) is among the most common causes of PH, there is an urge according to the 2021 European Society of Cardiology HF guidelines to find new biomarkers that aid in prognostication of this patient cohort. Given the role of tumour necrosis factor‐alpha (TNF‐α) in HF progression, we aimed to investigate the prognostic value of plasma proteins related to TNF‐α in patients with LHF‐PH, in relation to haemodynamic changes following heart transplantation (HT). Methods and results Twenty TNF‐α‐related plasma proteins were analysed using proximity extension assay in healthy controls (n = 20) and patients with LHF‐PH (n = 67), before and 1 year after HT (n = 19). Plasma levels were compared between the groups, and the prognostic values were determined using Kaplan–Meier and Cox regression analyses. Plasma levels of lymphotoxin‐beta receptor (LTBR), TNF receptor superfamily member 6B (TNFRSF6B), and TNF‐related apoptosis‐inducing ligand receptors 1 and 2 (TRAIL‐R1 and TRAIL‐R2, respectively) were higher in LHF‐PH pre‐HT vs. controls (P < 0.0001), as well as higher in pre‐HT vs. post‐HT (P < 0.001). The elevated pre‐HT levels of LTBR, TNFRSF6B, TRAIL‐R1, and TRAIL‐R2 decreased towards the levels of healthy controls after HT. Higher preoperative levels of LTBR, TNFRSF6B, TRAIL‐R1, and TRAIL‐R2 in LHF‐PH were associated with worse survival rates (P < 0.002). In multivariate Cox regression models, each adjusted for age and sex, LTBR, TNFRSF6B, TRAIL‐R1, and TRAIL‐R2 predicted mortality (P < 0.002) [hazard ratio (95% confidence interval): 1.12 (1.04–1.19), 1.01 (1.004–1.02), 1.28 (1.14–1.42), and 1.03 (1.02–1.04), respectively]. Conclusions Elevated pre‐HT plasma levels of the TNF‐α‐related proteins LTBR, TNFRSF6B, TRAIL‐R1, and TRAIL‐R2 in LHF‐PH decreased 1 year after HT, displaying a normalization pattern towards the levels of the healthy controls. These proteins were also prognostic, where higher levels were associated with worse survival rates in LHF‐PH, providing new insight in their potential role as prognostic biomarkers. Larger studies are warranted to validate our findings and to investigate their possible pathobiological mechanisms in LHF‐PH.

The development of a high-throughput genotyping platform with high quality, flexibility, and affordable genotyping cost is critical for marker-assisted breeding. In this study, a genotyping by target sequencing (GBTS) platform was developed in maize, which can be realized for a small number of markers (several to 5 K) through multiplex PCR (GenoPlexs) and for a large number of markers (1 to 45 K) through in-solution capture. The later was used for development of four SNP marker panels (GenoBaits Maize) containing 20 K, 10 K, 5 K, and 1 K markers. Two genotype panels, one consisting 96 representative worldwide maize inbred lines and the other containing 387 breeding lines developed in our maize breeding programs, were used to test and validate the developed marker panels. First, a 20 K SNP panel, with markers evenly distributed across maize genome, was developed from a 55 K SNP array with improved genome coverage. From this single marker panel, 20 K, 10 K, 5 K, and 1 K SNP markers can be generated by sequencing the samples at the average sequencing depths of 50×, 20×, 7.5×, and 2.5×, respectively. Highly consistent marker genotypes were obtained between the four marker panels and the 55 K array (over 95%) and between two biological replications (over 98%). Also, highly consistent phylogenetic relationships were generated by using four marker panels and two genotype panels, providing strong evidence for the reliability of SNP markers and GBTS genotyping platform. Cost-benefit analysis indicated that the genotypic selection cost based on the GBTS in maize was lower than phenotypic selection, allowing GBTS an affordable genotyping platform for marker-assisted breeding. Integration of this affordable genotyping platform with other breeding platforms and open-source breeding network would greatly facilitate the molecular breeding activities in small- and medium-size companies and developing countries. The four marker panels could be used for many fields of marker application, including germplasm evaluation, genetic mapping, marker-assisted selection (including genomic selection), and plant variety protection.

Follicular lymphoma (FL) is an indolent cancer of mature B-cells but with ongoing risk of transformation to more aggressive histology over time. Recurrent mutations associated with transformation have been identified; however, prognostic features that can be discerned at diagnosis could be clinically useful. We present here comprehensive profiling of both tumor and immune compartments in 155 diagnostic FL biopsies at single-cell resolution by mass cytometry. This revealed a diversity of phenotypes but included two recurrent patterns, one which closely resembles germinal center B-cells (GCB) and another which appears more related to memory B-cells (MB). GCB-type tumors are enriched for EZH2 , TNFRSF14 , and MEF2B mutations, while MB-type tumors contain increased follicular helper T-cells. MB-type and intratumoral phenotypic diversity are independently associated with increased risk of transformation, supporting biological relevance of these features. Notably, a reduced 26-marker panel retains sufficient information to allow phenotypic profiling of future cohorts by conventional flow cytometry. Follicular lymphoma can transform to a more aggressive histology. Here, the authors use bulk and single cell analysis to create a 26 marker panel which could be used to profile FL samples and predict the risk of transformation using flow cytometry.

Accurate identification of named accessions in germplasm collections is extremely important, especially for vegetatively propagated crops which are expensive to maintain. Thus, an inexpensive, reliable, and rapid genotyping method is essential because it avoids the need for laborious and time-consuming morphological comparisons. Single Nucleotide Polymorphism (SNP) marker panels containing large numbers of SNPs have been developed for many crop species, but such panels are much too large for basic cultivar identification. Here, we have identified a minimum set of SNP markers sufficient to distinguish apple cultivars held in the English and Welsh national collections providing a cheaper and automatable alternative to the markers currently used by the community. We show that SNP genotyping with a small set of well selected markers is equally efficient as microsatellites for the identification of apple cultivars and has the added advantage of automation and reduced cost when screening large numbers of samples.

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers. Metabolomics and lipidomics were performed using plasma of heart failure patients at stages A (n = 49), B (n = 61), and C+D (n = 26). Analysis of Variance (ANOVA) was used for screening dysregulated molecules. Bioinformatics was used to retrieve perturbed metabolic pathways. Univariate and multivariate receiver operating characteristic curve (ROC) analyses were used for potential biomarker screening. Stage A showed significant difference to other stages, and 142 dysregulated lipids and 134 dysregulated metabolites were found belonging to several metabolic pathways. Several marker panels were proposed for the diagnosis of heart failure stage A versus stage B-D. Several molecules, including lysophosphatidylcholine 18:2, cholesteryl ester 18:1, alanine, choline, and Fructose, were found correlated with B-type natriuretic peptide or left ventricular ejection fractions. In summary, using untargeted metabolomic and lipidomic profiling, several dysregulated small molecules were successfully identified between HF stages A and B-D. These molecules would provide valuable information for further pathological researches and biomarker development.

A high-throughput genotyping platform with customized flexibility, high genotyping accuracy, and low cost is critical for marker-assisted selection and genetic mapping in soybean. Three assay panels were selected from the SoySNP50K, 40K, 20K, and 10K arrays, containing 41,541, 20,748, and 9670 SNP markers, respectively, for genotyping by target sequencing (GBTS). Fifteen representative accessions were used to assess the accuracy and consistency of the SNP alleles identified by the SNP panels and sequencing platform. The SNP alleles were 99.87% identical between technical replicates and 98.86% identical between the 40K SNP GBTS panel and 10× resequencing analysis. The GBTS method was also accurate in the sense that the genotypic dataset of the 15 representative accessions correctly revealed the pedigree of the accessions, and the biparental progeny datasets correctly constructed the linkage maps of the SNPs. The 10K panel was also used to genotype two parent-derived populations and analyze QTLs controlling 100-seed weight, resulting in the identification of the stable associated genetic locus Locus_OSW_06 on chromosome 06. The markers flanking the QTL explained 7.05% and 9.83% of the phenotypic variation, respectively. Compared with GBS and DNA chips, the 40K, 20K, and 10K panels reduced costs by 5.07% and 58.28%, 21.44% and 65.48%, and 35.74% and 71.76%, respectively. Low-cost genotyping panels could facilitate soybean germplasm assessment, genetic linkage map construction, QTL identification, and genomic selection.