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Results for “transcriptomic platform”

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PubMed2004

[Establishment of CPP-SOM integrated cDNA microarray technology].

OBJECTIVE: To get an insight into the molecular mechanisms of diseases development and targeted therapy at the transcriptome level and search for potential therapeutic targets. METHODS: The present researchers established a cDNA microarray platform and applied component plane presentation integrated self-organizing map (CPP-SOM) to the microarray data obtained from a differentiation model, all trans retinoic acid-induced differentiation in NB4 cells. RESULTS: The platform included 12630 unique clones, including 9436 known genes. By CPP-SOM, the researchers were able to not only well classify the regulated genes into functionally distinct categories but also depict transcriptional changes throughout the process of the development of diseases or drug treatment. CONCLUSION: The platform has proven to be steady and reliable, and the CPP-SOM could serve as an important and good tool for analysis of microarray data.

Cell Line, Tumor↗

PubMed2003

Sputnik: a database platform for comparative plant genomics.

Two million plant ESTs, from 20 different plant species, and totalling more than one 1000 Mbp of DNA sequence, represents a formidable transcriptomic resource. Sputnik uses the potential of this sequence resource to fill some of the information gap in the un-sequenced plant genomes and to serve as the foundation for in silicio comparative plant genomics. The complexity of the individual EST collections has been reduced using optimised EST clustering techniques. Annotation of cluster sequences is performed by exploiting and transferring information from the comprehensive knowledgebase already produced for the completed model plant genome (Arabidopsis thaliana) and by performing additional state of-the-art sequence analyses relevant to today's plant biologist. Functional predictions, comparative analyses and associative annotations for 500 000 plant EST derived peptides make Sputnik (http://mips.gsf.de/proj/sputnik/) a valid platform for contemporary plant genomics.

Databases, Nucleic Acid↗

PubMed2026

Imaging-Guided Omics Technologies for Resolving Rare Cancer States and Advancing Nanomedicine.

The ability to resolve rare and transient cellular states is critical for understanding metastasis, immune evasion, and therapy resistance in cancer, yet these dynamic processes often escape detection by conventional sequencing and imaging approaches. Recent advances at the interface of nanotechnology, high-resolution live-cell imaging, and single-cell/spatial multiomics methods have enabled functional profiling of cells with unprecedented precision within their native microenvironment. In this Mini-Review, we highlight emerging nanoscale platforms that couple real-time phenotypic imaging with molecular readouts, such as FUNseq and CIN-seq, to directly link functional heterogeneity to transcriptomic, proteomic, and epigenomic information. By integrating nanoscale optical imaging, microengineered perturbation tools, and AI-driven computational analysis, these technologies open up new avenues for dissecting rare metastatic, therapy-resistant, or immune-evasive subpopulations. We further discuss how these next-generation imaging-guided single-cell and spatial omics platforms not only advance fundamental cancer biology but also create opportunities to accelerate the development of nanomedicine applications.

Humans↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2026

Representation learning for multi-modal spatially resolved transcriptomics data.

MOTIVATION: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. RESULTS: We introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g. 10x Genomics™, NanoString™) across multiple datasets. We achieved performance enhancement on structured tissues (e.g. brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a 2-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes. AVAILABILITY AND IMPLEMENTATION: AESTETIK is implemented in Python 3 and is available as open source software at http://www.github.com/ratschlab/aestetik. The Snakemake pipeline for reproducing the results is available at http://www.github.com/ratschlab/st-rep.

Spatial Transcriptomics↗

PubMed2026

Motor coordination and behavioural deficits in a mouse model of KMT2B-related dystonia.

INTRODUCTION: Pathogenic variants in KMT2B cause early-onset dystonia, but a mouse model that has undergone comprehensive, dystonia-oriented phenotyping is lacking. METHODS: We conducted detailed phenotyping on heterozygous Kmt2b constitutive knockout mice and wild-type littermates, assessing growth, neurobehavioural traits, motor coordination, sensorimotor gating, social behaviour and metabolic parameters, combined with striatal RNA sequencing. RESULTS: Kmt2b knockout mice of both sexes were viable but significantly smaller and lighter than littermate controls. Knockouts were hyperlocomotive in the open field and showed approximately two-fold larger acoustic startle responses; unexpectedly, prepulse inhibition was enhanced rather than reduced at all prepulse intensities. On the balance beam, knockouts crossed more slowly and paused more frequently; female knockouts also paused more on the ladder rung task. Frame-by-frame video analysis revealed a claw-like hindpaw posture characterized by abnormal inward flexion of the digits. Knockout mice spent less time investigating a novel conspecific, while social recognition memory remained intact. Striatal RNA sequencing confirmed reduction of Kmt2b transcript to approximately half of control levels and identified 177 differentially expressed genes, including Maob, encoding monoamine oxidase B; gene set enrichment analysis implicated neurodevelopmental, glial and mitochondrial processes. Nociception, vision, body-weight-adjusted grip strength, and clinical chemistry and haematological measures were largely unaffected. CONCLUSION: Heterozygous Kmt2b knockout mice show hyperlocomotion, altered sensorimotor gating, impaired motor coordination with dystonic-like paw posturing and reduced sociability, alongside a striatal transcriptomic signature implicating neurodevelopmental processes. The model mirrors aspects of human KMT2B-related dystonia and provides a platform for mechanistic study; environmental or pharmacological challenge may be needed to unmask overt dystonic features.

Dystonia↗

PubMed2006

Impact of intestinal colonization and invasion on the Entamoeba histolytica transcriptome.

A genome-wide transcriptional analysis of Entamoeba histolytica was performed on trophozoites isolated from the colon of six infected mice and from in vitro culture. An Affymetrix platform gene expression array was designed for this analysis that included probe sets for 9435 open reading frames (ORFs) and 9066 5' and 3' flanking regions. Transcripts were detected for > 80% of all ORFs. A total of 523 transcripts (5.2% of all E. histolytica genes) were significantly changed in amebae isolated from the intestine on Days 1 and 29 after infection: 326 and 109 solely on Days 1 and 29, and 88 on both days. Quantitative real-time reverse transcriptase PCR confirmed these changes in 11/12 genes tested using mRNA isolated from an additional six mice. Adaptation to the intestinal environment was accompanied by increases in a subset of cell signaling genes including transmembrane kinases, ras and rho family GTPases, and calcium binding proteins. Significant decreases in mRNA abundance for genes involved in glycolysis and concomitant increases in lipases were consistent with a change in energy metabolism. Defense against bacteria present in the intestine (but lacking from in vitro culture) was suggested by alterations in mRNA levels of genes similar to the AIG1 plant antibacterial proteins. Decreases in oxygen detoxification pathways were observed as expected in the anaerobic colonic lumen. Of the known virulence factors the most remarkable changes were a 20-35-fold increase in a cysteine proteinase four-like gene, and a 2-3-fold decrease in two members of the Gal/GalNAc lectin light subunit family. Control of the observed changes in mRNA abundance in the intestine might potentially rest with four related proteins with DNA binding domains that were down-regulated 6-16-fold in the intestinal environment. In conclusion, the first genome-wide analysis of the transcriptome of E. histolytica demonstrated that the vast majority of genes are transcribed in trophozoites, and that in the host intestine trophozoites altered the expression of mRNAs for genes implicated in metabolism, oxygen defense, cell signaling, virulence, antibacterial activity, and DNA binding.

Animals↗

PubMed2005

Microarray analysis detects differentially expressed genes in the pharyngeal region of mice lacking Tbx1.

22q11-deletion (DiGeorge/velocardiofacial) syndrome (22q11DS) is modeled by mutation of murine transcription factor Tbx1. As part of efforts to identify transcriptional targets of Tbx1, we analyzed the transcriptome of the pharyngeal region of Df1/+;Tbx1+/- embryos at 9.5 days of embryonic development using two independent microarray platforms. In this model, embryos are null for Tbx1, with hemizygosity of genes in cis with Tbx1 on one chromosome providing a positive control for array sensitivity. Reduced mRNA levels of genes deleted from Df1 were detected on both platforms. Expression level filtering and statistical analysis identified several genes that were consistently differentially expressed between mutant and wild type embryos. Real-time quantitative PCR and in situ hybridization validated diminished expression of Pax9 and Gcm2, genes known to be required for normal thymus and parathyroid gland morphogenesis, whereas Pax1, Hoxa3, Eya1, and Foxn1, which are similarly required, were not down-regulated. Gbx2, a gene required for normal arch artery development, was down-regulated specifically in the pharyngeal endoderm and the posterior part of pharyngeal arch 1, and is a potential point of cross talk between the Tbx1 and Fgf8 controlled pathways. These experiments highlight which genes and pathways potentially affected by lack of Tbx1, and whose role may be explored further by testing for epistasis using mouse mutants.

Animals↗

PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗

PubMed2005

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Biochemistry↗

PubMed2025

Transcript-guided targeted cell enrichment for scalable single-nucleus RNA sequencing.

Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

Animals↗

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↗

PubMed2005

Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data.

Genome-wide expression profiling is a powerful tool for implicating novel gene ensembles in cellular mechanisms of health and disease. The most popular platform for genome-wide expression profiling is the Affymetrix GeneChip. However, its selection of probes relied on earlier genome and transcriptome annotation which is significantly different from current knowledge. The resultant informatics problems have a profound impact on analysis and interpretation the data. Here, we address these critical issues and offer a solution. We identified several classes of problems at the individual probe level in the existing annotation, under the assumption that current genome and transcriptome databases are more accurate than those used for GeneChip design. We then reorganized probes on more than a dozen popular GeneChips into gene-, transcript- and exon-specific probe sets in light of up-to-date genome, cDNA/EST clustering and single nucleotide polymorphism information. Comparing analysis results between the original and the redefined probe sets reveals approximately 30-50% discrepancy in the genes previously identified as differentially expressed, regardless of analysis method. Our results demonstrate that the original Affymetrix probe set definitions are inaccurate, and many conclusions derived from past GeneChip analyses may be significantly flawed. It will be beneficial to re-analyze existing GeneChip data with updated probe set definitions.

Alleles↗

PubMed2002

Large-scale protein annotation through gene ontology.

Recent progress in genomic sequencing, computational biology, and ontology development has presented an opportunity to investigate biological systems from a unique perspective, that is, examining genomes and transcriptomes through the multiple and hierarchical structure of Gene Ontology (GO). We report here our development of GO Engine, a computational platform for GO annotation, and analysis of the resultant GO annotations of human proteins. Protein annotation was centered on sequence homology with GO-annotated proteins and protein domain analysis. Text information analysis and a multiparameter cellular localization predictive tool were also used to increase the annotation accuracy, and to predict novel annotations. The majority of proteins corresponding to full-length mRNA in GenBank, and the majority of proteins in the NR database (nonredundant database of proteins) were annotated with one or more GO nodes in each of the three GO categories. The annotations of GenBank and SWISS-PROT proteins are available to the public at the GO Consortium web site.

Animals↗

PubMed2026

Mapping convergent regulators of melanoma drug resistance by PerturbFate.

High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes. However, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference1 perturbations across the full spectrum of gene regulation, from chromatin remodelling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled more than 300,000 cultured melanoma cells to characterize multimodal phenotypic and gene regulatory responses to perturbations in more than 140 vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator complex components, linking module-specific biochemical properties to convergent transcriptional activations. We identified common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineated how perturbations in functionally unrelated genes reshape cell state. Thus, PerturbFate establishes a versatile platform for identifying key molecular regulators by anchoring multimodal regulatory dynamics to disease-relevant phenotypes.

Humans↗

PubMedDate not supplied

Computational systems analysis of developmental toxicity: design, development and implementation of a Birth Defects Systems Manager (BDSM).

Birth defects and developmental disabilities remain an important public health issue worldwide. With the availability of genomic sequences from a growing number of human and model organisms and the rapid expansion of the public repositories holding large-scale gene expression datasets, a computational systems analysis of developmental toxicology can incorporate this vast digital information toward the realization of predictive models for complex disease. Here we describe the initial design, development and implementation of a Birth Defects Systems Manager (BDSM). The project was motivated by the need for a computational-bioinformatics infrastructure to manage vast digital information from functional genomics and for a new knowledge environment specifically engineered for the analysis of developmental processes and toxicities. Proof-of-concept tested BDSM using meta-analysis of gene expression data collected from different laboratories, technology platforms, and study models. The composite dataset incorporated 232 microarray comparisons of RNA samples by single or dual microarray platforms, cDNA or oligonucleotide based probes, and human or mouse sequence information. Preliminary results identified system-level features in the embryonic transcriptome as it reacted to various developmental-teratological stimuli. BDSM is open access through the worldwide web (http://systemsanalysis.louisville.edu/) and can be integrated with other bioinformatics tools and resources to advance the pace of discovery in birth defects research.

Animals↗