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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.

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.

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.

Genomic prediction involves characterization of chromosome fragments in a training population to predict merit. Confidence in the predictions relies on their evaluation in a validation population. Many commercial animals are multibreed (MB) or crossbred, but seedstock populations tend to be purebred (PB). Training in MB allows selection of PB for crossbred performance. Training in PB to predict MB performance quantifies the potential for across-breed genomic prediction. Efficiency of genomic selection was evaluated for a trait with heritability 0.5 simulated using actual SNP genotypes. The PB population had 1,086 Angus animals, and the MB population had 924 individuals from 8 sire breeds. Phenotypic values were simulated for scenarios including 50, 100, 250, or 500 additive QTL randomly selected from 50K SNP panels. Panels containing various numbers of SNP, including or excluding the QTL, were used in the analysis. A Bayesian model averaging method was used to simultaneously estimate the effects of all markers on the panels in MB (or PB) training populations. Estimated effects were utilized to predict genomic merit of animals in PB (or MB) validation populations. Correlations between predicted and simulated genomic merit in the validation population was used to reflect predictive ability. Panels that included QTL were able to account for 50% or more of the within-breed genetic variance when the trait was influenced by 50 QTL. The predictive power eroded as the number of QTL increased. Panels that composed the QTL and no other markers were able to account for 50% or more genetic variance even with 500 QTL. Panels that included genomic markers as well as QTL had less predictive power as the number of markers on the panel was increased. Panels that excluded the QTL and relied on markers in linkage disequilibrium (LD) to predict QTL effects performed more poorly than marker panels with QTL. Real-life situations with 50K panels that excluded the QTL could account for no more than 20% genetic variation for 50 QTL and less than 10% for 500 QTL. The difference between panels that included and excluded QTL indicates that the predictive ability of existing panels is limited by their LD. Training in PB to predict MB tended to be more predictive than training in MB to predict PB due to greater average levels of LD in PB than in MB populations. Improved across breed prediction of genomic merit will require panels with more than 50,000 markers.

For lupus nephritis (LN) management, it is very important to detect fibrosis at an early stage. Urinary exosomal miRNAs profiling can be used as a potential multi-marker phenotyping tool to identify early fibrosis. We isolated and characterised urinary exosomes and cellular pellets from patients with biopsy-proven LN (n = 45) and healthy controls (n = 20). LN chronicity index (CI) correlated with urinary exosomal miR-21, miR-150, and miR-29c (r = 0.565, 0.840, −0.559, respectively). This miRNA profile distinguished low CI from moderate-high CI in LN patients with a high sensitivity and specificity (94.4% and 99.8%). Furthermore, this multimarker panel predicted an increased risk of progression to end-stage renal disease (ESRD). Pathway analysis identified VEGFA and SP1 as common target genes for the three miRNAs. Immunohistochemistry in LN renal biopsies revealed a significant increase of COL1A1 and COL4A1 correlated with renal chronicity. SP1 decreased significantly in the high-CI group (p = 0.002). VEGFA levels showed no differences. In vitro experiments suggest that these miRNA combinations promote renal fibrosis by increasing profibrotic molecules through SP1 and Smad3/TGFβ pathways. In conclusion, a urinary exosomal multimarker panel composed of miR-21, miR-150, and miR-29c provides a non-invasive method to detect early renal fibrosis and predict disease progression in LN.

Next-generation sequencing (NGS) technologies have enabled genome re-sequencing for exploring genome-wide polymorphisms among individuals, as well as targeted re-sequencing for the rapid and simultaneous detection of polymorphisms in genes associated with various biological functions. Therefore, a simple and robust method for targeted re-sequencing should facilitate genotyping in a wide range of biological fields. In this study, we developed a simple, custom, targeted re-sequencing method, designated \"multiplex PCR targeted amplicon sequencing (MTA-seq),\" and applied it to the genotyping of the model grass . To assess the practical usability of MTA-seq, we applied it to the genotyping of genome-wide single-nucleotide polymorphisms (SNPs) identified in natural accessions (Bd1-1, Bd3-1, Bd21-3, Bd30-1, Koz-1, Koz-3, and Koz-4) by comparing the re-sequencing data with that of reference accession Bd21. Examination of SNP-genotyping accuracy in 443 amplicons from eight parental accessions and an F progeny derived by crossing of Bd21 and Bd3-1 revealed that ~95% of the SNPs were correctly called. The assessment suggested that the method provided an efficient framework for accurate and robust SNP genotyping. The method described here enables easy design of custom target SNP-marker panels in various organisms, facilitating a wide range of high-throughput genetic applications, such as genetic mapping, population analysis, molecular breeding, and genomic diagnostics.