SEARCH · Search PubMed

Results for “high-throughput screen”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources

PubMed2026

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n = 91), with fewer studies examining samples from individuals of advanced reproductive age (n = 45) and fetal (n = 16) samples. Transcriptome analyses were most common (n = 103, 85%), followed by proteome (n = 19, 16%) and epigenome (n = 14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).

Female↗

PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗

PubMed2026

Targeted Next-Generation Sequencing for Improved Clinical Outcomes in People Living With Rare Diseases in Global South: Protocol for a Systematic Review and Meta-Synthesis.

BACKGROUND: Rare diseases affect many individuals and pose major challenges in diagnosis and treatment, especially in Global South countries where health care resources are limited. Targeted next-generation sequencing (NGS) has significantly advanced diagnostic accuracy and clinical care for rare diseases globally; however, its implementation and impact within the Global South context remain insufficiently studied. OBJECTIVE: This study aims to evaluate the use, clinical benefits, challenges, and implementation outcomes of targeted NGS for diagnosing and managing rare diseases in Global South populations. Specifically, it seeks to quantify the diagnostic yield of NGS, examine its influence on subsequent clinical decision-making, and identify principal barriers to, and facilitators of, the implementation of targeted NGS approaches in these contexts. METHODS: This protocol follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We will systematically search PubMed, Scopus, and Web of Science for studies published between 2005 and 2025 that report on the use of targeted NGS in Global South population with rare diseases. Two reviewers will independently perform study selection, data extraction, quality assessment, and evaluation of risk of bias by using QUADAS-2 for diagnostic accuracy studies and the risk of bias assessment tool for nonrandomized studies. Meta-analyses will be conducted to estimate pooled outcomes for diagnostic yield, with heterogeneity assessed using random effects models. Heterogeneity will be further examined through visual inspection of forest plots and by evaluating the chi-square test and I² statistic. RESULTS: The protocol has been registered with PROSPERO (CRD420251078455). Database search or screening, data extraction, and data synthesis are planned to commence in June 2026 and conclude by September 2026. Study findings will synthesize the diagnostic yield, clinical impact, and contextual determinants influencing the implementation of targeted NGS in Global South health care settings. CONCLUSIONS: This review will provide evidence on the application, advantages, limitations, and clinical outcomes of targeted NGS for individuals affected by rare diseases in countries of the Global South. The finding will identify priorities for capacity strengthening, policy development, and future genomic research. TRIAL REGISTRATION: PROSPERO CRD420251078455; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251078455. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/85150.

Rare Diseases↗

PubMed2026

Upscaling Genotyping by Amplicon Sequencing With GBAS-GUI.

Genotyping by amplicon sequencing (GBAS) is a relatively low-cost approach for generating genotypic data compared with established genomic methods, making it highly scalable and particularly suitable for large-scale genetic monitoring projects. However, most existing analytical pipelines are either marker-specific, insufficiently scalable, or lacking efficient data management systems for the long-term integration of genotypic information, limiting the full potential of GBAS. Here, we address this gap by introducing GBAS-GUI (https://github.com/sonnenbe-dot/GBAS-GUI), a pipeline capable of generating GBAS-based genotypic data for a wide variety of loci at scale. GBAS-GUI integrates a graphical user interface with multiple checkpoints to improve accessibility and robustness. It implements multiprocessing architecture and a relational database that links genotypic data with associated sample metadata to enhance scalability and data management. The pipeline further enables marker screening through automated calculation of polymorphism information content (PIC) and implements a strategy to recover homologous genotypic information from paralogous loci with non-overlapping amplicon length ranges. Using multiple empirical datasets, we demonstrate substantial improvements in processing speed, database management and handling artefacts related to co-amplification of unspecific regions and duplicates of the same genomic region. We further show that incorporating the full sequence information captured by an amplicon increases marker information content beyond what is achievable with length-based genotyping alone and expands the analytical versatility of GBAS. Overall, GBAS-GUI provides a robust, scalable and versatile framework that unlocks the potential of GBAS for large-scale population genetic and phylogeographic studies.

Genotyping Techniques↗

PubMed2026

Recurrent and niche-specific functional bacteriome of maize hybrid revealed by integrated metabarcoding and culturomics.

The plant microbiome plays a pivotal role in plant survival in natural habitats by facilitating nutrient acquisition, stress adaptation, and disease suppression, while also offering opportunities to enhance crop productivity and climate resilience. However, the distribution of persistent and culturable bacteriome across maize-associated niches and their functional potential remain poorly resolved. This study integrated metagenomic next-generation sequencing (mNGS-based metabarcoding) and culturomics to characterise the maize-associated bacteriome of bulk soil, rhizoplane, phylloplane, and cob of the maize hybrid PHM-1 under contrasting cropping and tillage systems, and to identify recurrent and agriculturally promising bacteriome components. The bacteriome exhibited pronounced niche-specific structuring, whereas overall bacterial community composition did not differ significantly across cropping and tillage treatments (ANOSIM, R = 0.038, p = 0.306). Proteobacteria predominated in the culturable bacteriome (69-84%; mean, 76.2%) but accounted for only 1% of the total bacteriome, whereas Patescibacteria and Firmicutes were relatively enriched. Niche-specific dominance was evident, with Pantoea accounting for 40.79% of the total and 56.27% of the culturable phylloplane bacteriome under cereal monocropping, while Serratia represented 31.59% and 59.40% of the total and culturable cob bacteriomes, respectively. Across niches, mNGS captured substantially greater bacteriome diversity, particularly uncultured and unidentified taxa in soil-associated compartments, whereas culturomics recovered a narrower but functionally accessible fraction. Culturomics yielded 99 isolates representing 32 species across 12 genera, including six genera shared with the mNGS-derived recurrent bacteriome: Bacillus, Enterobacter, Pantoea, Pseudomonas, Serratia, and Stenotrophomonas. Functional screening identified strong biocontrol and plant-beneficial traits among core-associated isolates. Pseudomonas oryzihabitans ZM-DL-PA10 inhibited Rhizoctonia solani, Macrophomina phaseolina, and Bipolaris maydis by up to 40.6%, 43.9%, and 45.2%, respectively, through secreted and volatile metabolites; exhibited P, K, and Zn solubilisation; and produced IAA and siderophores. It also recorded the lowest B. maydis disease index (ADI) of 1.00. Pantoea ananatis ZM-BH-EA4 showed 52.4% and 68.5% inhibition of R. solani and B. maydis, respectively, through volatile metabolites. Collectively, the integration of mNGS and culturomics revealed a strongly compartmentalised maize bacteriome and identified recurrent, culturable, and functionally promising bacterial taxa, providing a targeted resource for microbiome-based crop protection and climate-resilient maize production.

Zea mays↗

PubMed2026

Next-Generation Sequencing Methods for Sensitive Hepatitis B Viral Genome Analysis: A European Study.

This multicentre study investigated the utility of next-generation sequencing (NGS) to detect and generate hepatitis B virus (HBV) genomes in samples of low viral load (from 0.2 to 6207 IU/mL). 23 HBV DNA-positive plasma samples of genotypes A-E and one HBV-negative control sample were assayed blindly via 9 established NGS methods from 6 European laboratories. Methods included untargeted metagenomics, pre-enrichment by probe-capture followed by Illumina sequencing, and HBV-specific PCR pre-amplification followed by sequencing with Nanopore or Illumina. Full HBV genomes were obtained only from samples with viral loads > 1000 IU/mL using probe-capture methods, > 200 IU/mL using PCR-Illumina methods, > 10 IU/mL using PCR-Nanopore methods, and in no samples using metagenomic methods. Contamination was observed in the negative control and samples with very low viral loads in PCR-based methods. Probe-capture and metagenomic methods detected additional viruses not routinely screened in blood donations, including polyomaviruses and herpesviruses; positive results were confirmed by PCR. In conclusion, NGS may delineate whole-genome sequences at low viral loads if supported by a PCR pre-amplification step. Probe-capture methods also reliably detect HBV without pre-amplification but show limited genome coverage for samples with low viral loads; they may additionally detect a wide range of blood-borne viruses.

Humans↗

PubMed2026

The diagnostic potential of combined quantitative polymerase chain reaction and next-generation sequencing using the same primers for periprosthetic joint infection.

Next-generation sequencing (NGS) enables the detection of specific pathogens unidentifiable by conventional cultures, but its application in orthopedics remains inconsistent due to background contamination and irreproducible findings. This study evaluated the diagnostic performance of a novel workflow combining broad-range 16S rRNA gene quantitative PCR (qPCR) screening with downstream NGS, focusing on bacterial biomass thresholds. The qPCR assay demonstrated excellent intrarater reliability, with an intraclass correlation coefficient (ICC) of 0.961 (95% confidence interval, 0.881 to 0.997). Based on serially diluted positive controls, a quantitative threshold of 10⁵ CFU/mL was established as the minimum concentration required for the consistent detection of fastidious taxa, such as Escherichia coli. When evaluated against conventional cultures using 95 sonicate fluid and 276 pre/intraoperative tissue samples, the qPCR assay achieved a sensitivity of 80% and a specificity of 72%. Subsequent NGS sequencing of 26 clinical samples and 9 controls showed concordance in 4 of 6 culture-positive infected cases with NGS taxonomy, whereas the remaining discrepancies were likely attributable to culture-based phenotypic misidentification. Notably, among the qPCR-positive cases, three were culture-negative, including two hip prosthesis loosening cases exhibiting polymicrobial profiles, and one post-traumatic osteoarthritis case harboring low-level Staphylococcus. Crucially, this post-traumatic patient developed delayed periprosthetic joint infection (PJI) 2 years post-surgery, with cultures identifying Staphylococcus previously detected by the initial NGS analysis. Integrating qPCR screening with targeted NGS effectively refines pathogen identification, filters environmental artifacts, and overcomes the diagnostic limitations of culture-negative infections in orthopedic practice.IMPORTANCENext-generation sequencing (NGS) enables the detection of specific pathogens in clinical samples that are not identifiable by conventional methods. However, NGS applications in orthopedics have not been quantitatively evaluated, and findings have been inconsistent owing to contaminants and the presence of non-credible causative organisms. These factors primarily stem from the failure to evaluate low-biomass samples and the absence of proper controls, such as negative controls or mock community DNA samples. This study demonstrates that interpreting results from low-biomass samples requires careful consideration because NGS relies on relative bacterial abundances; distinguishing likely pathogens from contaminants is particularly challenging when bacterial loads are low. We demonstrated that combining NGS with quantitative PCR (qPCR) and applying a Cq cutoff can reduce false positives.

Humans↗

PubMed2026

CRISPRessoSea: streamlined analysis and comparison of pooled amplicon CRISPR screens.

BACKGROUND: CRISPR genome editing enables precise modification of genomic targets but may also induce unintended edits at off-target sites with similar sequences. Pooled amplicon sequencing can assess on- and off-target editing across many samples, yet analyzing, aggregating, and visualizing results from multiple pooled experiments remains challenging. Tools to simplify and standardize these analyses are needed to provide reproducible and comparable interpretation of editing data. RESULTS: We developed CRISPRessoSea, a software package that processes, compares, and visualizes genome editing rates from pooled amplicon sequencing experiments. The tool provides standardized workflows for analyzing editing across multiple targets and samples, supports both nuclease- and base-editing modalities, and generates clear, data-rich summaries suitable for downstream interpretation. CONCLUSIONS: CRISPRessoSea facilitates reproducible, scalable analysis of CRISPR editing outcomes across diverse experimental designs, enabling more efficient and transparent assessment of genome editing specificity. The software is freely available at https://github.com/clementlab/CRISPRessoSea .

Software↗

PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed2026

Clinical and genomic characterization of Influenza A co-infection with SARS-CoV-2 and Influenza B: a respiratory surveillance study in Assam, India.

Influenza and SARS-CoV-2 are the primary contributors to seasonal respiratory infections and frequently co-circulate, creating significant health challenges. The present respiratory surveillance study was conducted in Dibrugarh, Assam, India from January 2025 to August 2025 to investigate the genomic characteristics of circulating viruses and identify potential co-infections. Overall, 4,948 respiratory samples were screened using multiplex real-time PCR, followed by subtyping of Influenza A and Influenza B. Next-generation sequencing (NGS) was performed in selected positives of SARS-CoV-2 and Influenza A. Genomic analysis included mutational profiling, phylogenetic analysis and N-glycosylation site prediction using bioinformatics tools. Two co-infection cases were detected: one involving Influenza A (H3N2) with SARS-CoV-2 (Omicron XFG lineage) and another involving Influenza A (H3N2) with Influenza B (Victoria lineage). Both patients experienced mild illness without hospitalisation. NGS revealed that the Influenza A (H3N2) viruses belonged to clade 3C.2a1b.2a.2a.3a.1 while SARS-CoV-2 sequence was classified under the Omicron XFG lineage. Mutational analysis of the HA gene showed several amino acid differences compared to the reference vaccine strain A/Darwin/6/2021. N-glycosylation analysis predicted conserved sites at positions 79, 181, 262, and 301 in all strains along with an additional predicted site at position 110 in both co-infection cases. Although the co-infection cases presented with mild clinical manifestations, the observed genomic variations indicate a potential role of co-infecting viruses in shaping viral evolution. Given the limited genomic data available from Northeast India, the study underscores the need for sustained large scale follow up and genomic surveillance to monitor emerging mutations and target future vaccine strategies.

Humans↗

PubMed2026

Benchmarking DNA extraction protocols across use cases for culture-independent Nanopore metagenomics.

Oxford Nanopore Technologies (ONT) sequencing offers several advantages for metagenomics, including long reads, rapid turnaround, low upfront cost, scalability and portability. However, for ONT metagenomics, DNA yield, quality and integrity are important considerations when selecting an extraction method. Many metagenomic extraction methods use harsh lysis conditions to extract a wide range of species and provide an accurate community composition, but these conditions can compromise DNA fragment length. Therefore, extraction methods for ONT metagenomics must balance DNA shearing and recovery with representative community lysis. We systematically evaluated DNA extraction methods for ONT metagenomic sequencing using a use case-oriented framework. Among nearly 50 extraction methods screened, 7 were selected for detailed comparison based on suitability for metagenomics, variation in methodology, availability, cost and processing time: Norgen BioTek Corp's Stool DNA Isolation (NG), Zymo Research's ZymoBIOMICS Quick-DNA HMW MagBead (ZMG), Qiagen's DNeasy Blood and Tissue (QBT), Macherey-Nagel's NucleoMag DNA Microbiome (MN), Zymo Research's ZymoBIOMICS DNA Mini Prep (ZMI), Qiagen's DNeasy PowerSoil/QIAamp PowerFecal Pro (PS) and Qiagen's QIAamp Fast DNA Stool Mini (QIA). Methods were tested using Zymo Research's ZymoBIOMICS Microbial Community Standard (MCS), a matrix-free mock community with known composition. DNA extracts were sequenced on an ONT PromethION using the Rapid Barcoding Kit, except QIA due to insufficient DNA yield. Metrics for the method, DNA extracts, sequencing and genomes were evaluated, revealing trade-offs between methods. The two magnetic bead methods, MN and ZMG, produced the highest mean read length N50 values (13.9 and 16.5 kb, respectively) but showed apparent community compositions skewed towards Gram-negative bacteria. In contrast, ZMI and PS maintained a community composition close to expected, with reduced mean read length N50 values (4.5 vs. 7.5 kb). Performance across various metrics is presented in the context of the following use cases: maximizing genome coverage and assembly completeness, preserving composition accuracy, targeting specific species and limiting required resources (equipment, time or budget). The metrics and use case considerations presented offer practical guidance for informed selection of DNA extraction methods for ONT metagenomics. For accurate community composition, ZMI or PS are recommended, while PS and ZMG perform best at maximizing genome coverage and assembly completeness. NG and QBT may be the most economical options, though performance trade-offs were observed. Finally, PS may be the preferred method for time-sensitive diagnostic or field applications.

Metagenomics↗

PubMed2025

Identification of intragenic variants in pediatric patients with intellectual disability in Peru.

BACKGROUND: Intellectual disability in Latin America can reach a frequency of 12% of the population, these may include nutritional deficiencies, exposure to toxic or infectious agents, and the lack of universal neonatal screening programs. In 90% of patients with intellectual disability, the etiology can be attributed to variants in the genome. OBJECTIVE: to determine intragenic variants in patients with intellectual disability between 5 and 18 years old at Instituto Nacional de Salud del Niño. METHODS: It is a descriptive cross-sectional study with convenience sampling. A total of 124 children diagnosed with intellectual disability were selected based on psychological test results and availability for whole exome sequencing. In addition, a chromosomal analysis of 6.55 M was performed on ten patients with a negative result in sequencing. Relative and absolute frequencies and measures of central tendency and dispersion were determined according to their nature. In addition, multiple linear regression and Poisson regression were used to determine the association between some clinical characteristics and the probability of occurrence in patients with positive results. RESULTS: The median age of the patients was 6.3 (IQR = 5.95), males accounted for 57.3%, and 91.9% of the cases had mild intellectual disability. Exome sequencing determined the etiology in 30.6% of patients with intellectual disability, of which 52.6% were autosomal dominant inheritance. The most frequent genes found were MECP2, STXBP1 and LAMA2. A broad genotype-phenotype correlation was identified, highlighting the genetic heterogeneity of intellectual disability in this population. The presence of dermatologic lesions, dystonia, peripheral neurological disorders, and fourth finger flexion limitation were observed more frequently in patients with intellectual disability with "positive results". CONCLUSIONS: This study shows that one-third of patients with intellectual disability exhibit intragenic variants, highlighting the importance of genetic analysis for accurate diagnosis. The identification of genes such as MECP2, STXBP1, and LAMA2 underscores the genetic heterogeneity of intellectual disability in the studied population. These findings emphasize the need for genetic testing in clinical management and the implementation of early detection programs in Peru.

Humans↗

PubMed2026

Novel and High-Throughput Method of Isolating Single Fetal Cells Using FACS for NIPT.

OBJECTIVE: To evaluate fluorescence activated cell sorting (FACS) as a method of single-cell isolation of rare circulating fetal cells from maternal blood for use in cell-based non-invasive prenatal testing (cbNIPT). METHOD: Blood samples (30 mL) were collected from 75 'low-risk' pregnant women (gestational age 10-15 weeks). Fetal cells were enriched and stained using magnetic activated cell sorting. Following enrichment, single fetal cells were sorted in individual PCR tubes by FACS. After cell lysis, verification of fetal cell origin was performed using short tandem repeat (STR) analysis with the GlobalFiler PCR Amplification kit. RESULTS: An average of 13.7 cells were sorted using FACS. STR analysis identified 8.2 fetal cells on average, representing 60.2% of the sorted cells. The four-step single-cell isolation procedure facilitated an overall enrichment of approximately 16-million-fold. One sample did not render any fetal cell, corresponding to 1.3% of the samples. CONCLUSION: FACS, which is typically used for segregation of large populations of cells, can be used for single-cell isolation of rare fetal cells in an automated setup. This not only helps in making cell isolation faster and high throughput but also provides fetal cells for a more comprehensive genetic analysis of the fetus.

Humans↗

PubMed2026

Functional screening and single-cell cultivation of marine CO2-fixing bacteria via flow-mode Raman-activated cell sorting.

Most marine CO2-fixing microorganisms remain uncultivated due to strong culture bias and low throughput of conventional approaches, which fail to link in situ function with isolated strains and render slow-growing or low-abundance taxa virtually inaccessible. This study presents an integrated single-cell workflow that incorporates 13C-NaHCO3 labeling, high-throughput flow-mode Raman-activated cell sorting (RACS) and microwell cultivation for the isolation of active CO2-fixing bacteria from the Yellow Sea. Function-guided sorting was achieved by monitoring the 13C-induced Raman shifts of carotenoids (ν1 band: ∼1507 to ∼ 1503.78 cm-1 at 24 h). Genomic and physiological analyses identified Paraburkholderia aromaticivorans FR-4 as a novel facultative chemoautotrophic nitrite-oxidizing bacterium (NOB). Its genome encodes complete nitrite oxidation and Calvin cycle pathways, together with key carbon acquisition genes (carbonic anhydrase, bicarbonate transporter). FR-4 grows autotrophically using NO2- as the electron donor and CO2/HCO3- as the carbon source, confirming its ability to couple nitrite oxidation with carbon fixation, while retaining metabolic flexibility for heterotrophic growth. By directly linking in situ carbon-fixing activity, genotype, and phenotype, this workflow provides a targeted strategy for exploring elusive marine CO2-fixing bacteria and overcomes critical limitations of conventional cultivation.

Carbon-fixing↗

PubMed2026

Sequencing approaches in hereditary cancer testing: strengths, limitations and future directions.

Over the past three decades, Hereditary Cancer Testing (HCT) has evolved from single gene assays into multigene panel testing (MGPT), which allows for the screening of all known hereditary cancer genes in a single assay. MGPT is currently the standard approach for clinical HCT. However, with decreasing sequencing costs and increased instrument throughput, the scalability of exome sequencing (ES) and genome sequencing (GS) for HCT indications is becoming more viable. These methods provide broader insights into the coding exons and/or the entire genome, respectively. ES/GS data can also be reanalyzed to identify variants in novel genes that were not characterized at the time of initial testing, or to support research efforts aimed at uncovering additional associations between germline variants and cancer predisposition. Additionally, the emerging use of long-read sequencing (LRS) is noteworthy, enabling improved variant detection compared to short-read sequencing, especially for complex/structural variants and variation in difficult-to-sequence or paralogous regions in genes such as PMS2. This has the potential to increase the accuracy of HCT, reduce the turnaround time, find previously unidentifiable cancer risk variants, and ultimately increase the diagnostic yield. This article provides a comprehensive summary of the sequencing approaches used in HCT, discussing their strengths and limitations. We also highlight the added value of complementing DNA-only testing with RNA and tumor sequencing. Furthermore, we explore LRS-based approaches and discuss opportunities for their implementation in routine genetic testing for hereditary cancer.

Humans↗

PubMed2025

A pluripotent stem cell atlas of multilineage differentiation.

Human pluripotent stem cells offer a scalable platform to study genetic and signalling mechanisms governing cell lineage decisions during differentiation. Genome-wide and single-cell transcriptomics technologies likewise offer high-throughput analysis of heterogeneous cell differentiation states. While in vivo development has been extensively characterised using these technologies, there remains a need for comprehensive single-cell transcriptomic profiling of stem cell differentiation from pluripotency. Understanding gene expression changes governing differentiation in vitro is key to developing high fidelity differentiation protocols and understanding fundamental mechanisms of development. We generated a single-cell RNA sequencing time course to study the role of developmental signalling pathways on multilineage diversification from pluripotency in vitro. The combined dataset of over 60,000 cells spans cell types from a time course of differentiation across all germ layers, ranging from gastrulation cell states to progenitor and committed cell types. These data provide a diverse benchmarking reference point to compare against in vivo development and advance understanding of signalling regulation of differentiation, providing insights into protocol development, drug screening, and regenerative medicine applications.

Pluripotent Stem Cells↗

PubMed2026

A homogeneous immunoassay based on AlphaLICA technology for detecting florfenicol residues in animal-derived foods.

Florfenicol (FF), a broad-spectrum amide antibiotic widely used in livestock, poultry, and aquaculture, poses potential threats to food safety and public health due to its residual accumulation. In this study, a novel homogeneous immunoassay based on Amplified Luminescent Proximity Homogeneous Assay (AlphaLICA) technology was developed for the first time for rapid screening of FF residues in milk and egg matrices. By covalently immobilizing the FF-BSA conjugate and goat anti-mouse IgG onto luminescent and photosensitive microspheres, respectively, the method achieved wash-free, homogeneous quantitative detection through a competitive immunoreaction. Under optimized conditions, the assay exhibited a linear range of 0.2-16.2 ng mL-1, with a limit of detection of 9.7 pg mL-1 and a limit of quantification of 183 pg mL-1. The intra- and inter-batch coefficients of variation ranged from 3.08% to 5.70% and 2.44% to 7.09%, respectively. Spike recovery rates in milk and egg matrices ranged from 93.18% to 107.17% (RSD ≤ 5.57%). Cross-reactivity with 11 other common antibiotics, including chloramphenicol and thiamphenicol, was below 0.1%, demonstrating excellent specificity. Comparative analysis with a commercial ELISA kit showed high consistency (r2 = 0.9332, p < 0.001). With high sensitivity, strong specificity, simple operation, and a detection time of only 10 min, this method provides a reliable technical platform for high-throughput, rapid monitoring of FF residues in milk and egg matrices.

Journal Article↗

PubMed2004

High-throughput method for detecting genomic-deletion polymorphisms.

DNA microarrays have been successfully used with different microorganisms, including Mycobacterium tuberculosis, to detect genomic deletions relative to a reference strain. However, the cost and complexity of the microarray system are obstacles to its widespread use in large-scale studies. In order to evaluate the extent and role of large sequence polymorphisms (LSPs) or insertion-deletion events in bacterial populations, we developed a technique, termed deligotyping, which hybridizes multiplex-PCR products to membrane-bound, highly specific oligonucleotide probes. The approach has the benefits of being low cost and capable of simultaneously interrogating more than 40 bacterial strains for the presence of 43 genomic regions. The deletions represented on the membrane were selected from previous comparative genomic studies and ongoing microarray experiments. Highly specific probes for these deletions were designed and attached to a membrane for hybridization with strain-derived targets. The targets were generated by multiplex PCR, allowing simultaneous amplifications of 43 different genomic loci in a single reaction. To validate our approach, 100 strains that had been analyzed with a high-density microarray were analyzed. The membrane accurately detected the deletions identified by the microarray approach, with a sensitivity of 99.9% and a specificity of 98.0%. The deligotyping technique allows the rapid and reliable screening of large numbers of M. tuberculosis isolates for LSPs. This technique can be used to provide insights into the epidemiology, genomic evolution, and population structure of M. tuberculosis and can be adapted for the study of other organisms.

DNA Probes↗