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PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2025

Multiomics Analysis Reveals Therapeutic Targets for Chronic Kidney Disease With Sarcopenia.

BACKGROUND: The presence of sarcopenia in patients with chronic kidney disease (CKD) is associated with poor prognosis. The mechanism underlying CKD-induced muscle wasting has not yet been fully explored. This study investigates the influence of renal secretions on muscles using multiomics sequencing. METHODS: The kidney transcriptome analysis by RNA-seq and protein profiling by tandem mass tag (TMT), serum TMT and muscle TMT were performed in CKD established using 0.2% adenine and control mice. Spp1 recombinant protein was used to study its effect on myotube atrophy in&#xa0;vitro. In animal experiments on CKD, pharmacological inhibition of Spp1 was used to explore the role of Spp1 in skeletal muscle wasting. Transcriptome analysis was performed to identify differentially expressed genes (DEGs) in the gastrocnemius muscle following Spp1 pharmacological inhibition. RESULTS: In the renal transcriptome and TMT, 503 and 377 proteins/genes respectively were co-upregulated and co-downregulated. In the serum TMT of CKD and normal control (NC) mice, 22 upregulated and 7 downregulated differentially expressed proteins (DEPs) showed the same expression patterns as those in the kidney transcriptome and TMT analysis. Based on bioinformatics analysis and reported studies, we selected Spp1 for further validation. Spp1 recombinant protein was added to C2C12 myotubes in&#xa0;vitro, and the results indicated that Spp1 significantly increased the protein levels of the muscle atrophy marker (Murf-1) and promoted the smaller myotubes (all p&#x2009;<&#x2009;0.05). Compared with NC mice, Spp1 mRNA and protein levels were significantly upregulated in the kidneys of CKD mice, and the serum concentration of Spp1 was also markedly increased (all p&#x2009;<&#x2009;0.05). In animal experiments, pharmacological inhibition of Spp1 increased the weights of gastrocnemius and tibialis anterior muscles (p&#x2009;<&#x2009;0.05) and improved muscle atrophy phenotype. Transcriptome analysis showed that DEGs in the gastrocnemius muscle following Spp1 pharmacological inhibition were enriched in protein digestion and absorption, glucagon signalling pathway, apelin signalling pathway and ECM-receptor interaction pathway. CONCLUSIONS: Our study is the first to establish a regulatory network of kidney-muscle crosstalk to explore the potential mechanism of CKD-related sarcopenia. Employing multiomics analysis, cellular assessment and animal experiments, we have identified that Spp1 could potentialy serve as a promising therapeutic target for CKD patients with sarcopenia.

Sarcopenia↗
PubMed2026

Thyroid hormone deprivation creates an immunological signature in the mouse liver, involving Kupffer cell presentation as the mouse ages.

PURPOSE: Aging is associated with an increased prevalence of chronic liver diseases suggesting impaired immune and metabolic function. In addition, thyroid hormone (TH) impacts liver physiology and TH deprivation or excess negatively affect organ maintenance. However, whether age-dependent consequences of TH alterations are reflected in a liver-specific adaptation is unknown so far. The present study aimed to characterize the impact of TH deprivation or excess on the liver transcriptome during aging. METHODS: Five- and 21-month-old male C57BL/6 mice were exposed either to chronic TH deprivation or to chronic TH excess and compared to control treatment by microarray-based liver transcriptome analysis. RESULTS: Significant roles of both TH state and age became obvious: Bioinformatic analysis of the liver transcriptome data revealed an age-dependent immune signature by chronic TH deprivation, an age-dependent immune and metabolic signature independent of exogenous TH modulation, as well as an age-dependent metabolic signature by chronic TH excess. Published data of single cell transcriptomic atlas characterizing aging tissues in the mouse were compared with our data and revealed Kupffer cell presentation in the immunological signature by TH deprivation during aging. Literature data for four prominent differentially expressed genes, namely C1qb, C3ar1, Ctss, and Msr1, revealed that the complement system, extracellular matrix remodelling, as well as the proinflammatory phenotype of Kupffer cells are altered by TH deprivation during aging. CONCLUSION: In conclusion, our study illuminates the interplay between TH deprivation, aging, and liver transcriptome signatures, highlighting potential implications for immune function and tissue maintenance, particularly through the modulation of Kupffer cell presentation.

Animals↗
PubMed2026

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.

Humans↗
PubMed2007

Molecular portrait of high productivity in recombinant NS0 cells.

Many important therapeutic proteins are produced in recombinant mammalian cells. Upon the introduction of the product gene, the isolated clones typically exhibit a wide range of productivity and high producers are subsequently selected for use in production. Using DNA microarray, two-dimensional gel electrophoresis (2DE), and iTRAQ as global surveying tools, we examined the transcriptome and proteome profiles of 11 lines of NS0 cells producing the same antibody molecule. Genes that are significantly differentially expressed between high and low producer groups statistically fall into a number of functional classes. Their distribution among the functional classes differs somewhat between transcriptomic and proteomic results. Overall, a high degree of consistency between transcriptome and proteome analysis are seen, although some genes exhibiting inconsistent trends between transcript and protein levels were observed as expected. In a novel approach, functional gene networks were retrieved using computational pathway analysis tools and their association with productivity was tested by physiological comprehension of the possible pathways involved in high recombinant protein production. Network analysis indicates that protein synthesis pathways were altered in high producers at both transcriptome and proteome levels, whereas the effect on cell growth/death pathways was more prominent only at the transcript level. The results suggest a common mechanism entailing the alteration of protein synthesis and cell growth control networks leading to high productivity. However, alternate routes with different sets of genes may be invoked to give rise to the same mechanistic outcomes. Such systematic approaches, combining transcriptomic and proteomic tools to examine high and low producers of recombinant mammalian cells will greatly enhance our capability to rationally design high producer cells. This work is a first step towards shedding a new light on the global physiological landscape of hyper productivity of recombinant cells.

Animals↗
PubMed2002

Human prostate epithelial cell-type cDNA libraries and prostate expression patterns.

BACKGROUND: Transcriptome analysis is a powerful approach to uncovering genes responsible for diseases such as prostate cancer. Ideally, one would like to compare the transcriptomes of a cancer cell and its normal counterpart for differences. METHODS: Prostate luminal and basal epithelial cell types were isolated and cell-type-specific cDNA libraries were constructed. Sequence analysis of cDNA clones generated 505 luminal cell genes and 560 basal cell genes. These sequences were deposited in a public database for expression analysis. RESULTS: From these sequences, 119 unique luminal expressed sequence tags (ESTs) were extracted and assembled into a luminal-cell transcriptome set, while 154 basal ESTs were extracted and assembled into a basal-cell set. Interlibrary comparison was performed to determine representation of these sequences in cDNA libraries constructed from prostate tumors, PIN, cell lines. CONCLUSIONS: Our analysis showed that a significant number of epithelial cell genes were not represented in the various transcriptomes of prostate tissues, suggesting that they might be underrepresented in libraries generated from tissue containing multiple cell types. Although both luminal and basal cell types are epithelial, their transcriptomes are more divergent from each other than expected, underscoring their functional difference (secretory vs. nonsecretory). Tumor tissues show different expression of luminal and basal genes, with perhaps a trend towards expression of basal genes in advanced diseases.

DNA, Complementary↗
PubMed2004

Global analysis of gene expression in mammalian kidney.

The past decade has seen the achievement of sequencing of human, rat and mouse genomes and the development of high-throughput methods for quantitative monitoring of gene expression. The aim of the beginning post-genomic era is to determine the function of all these genes, a challenge in which the community of physiologists should have a leading role. In this short review, we discuss the relevance, feasibility and impact of transcriptome analysis in renal physiology. Comparison of transcriptomes demonstrates that cell lines are poor physiological models, making it necessary to work on native kidney tissue. Palliating kidney heterogeneity therefore requires the development of methods for transcriptome analysis sensitive enough to be compatible with microdissected nephron segments. Axial comparison of transcriptomes along the human nephron unexpectedly points out that the segmentation of nephrons concerns not only genes involved in solute and water transport functions and their regulation, but also genes related to the control of cell division, differentiation and apoptosis. Comparison of transcriptome of a same nephron segment from mice under different pathophysiological conditions outlines the wide pleiotropy of kidney function regulations. Both types of comparative studies also identify yet unknown transcripts specifically expressed along the nephron or under pathophysiological conditions.

Animals↗
PubMed2004

Molecular markers from the transcribed/expressed region of the genome in higher plants.

In recent years, molecular marker technology in higher plants has witnessed a shift from the so-called random DNA markers (RDMs), developed in the past arbitrarily from genomic DNA and cDNA, to the molecular markers representing the transcriptome and the other coding sequences. These markers have been described as gene targeted markers (GTMs). Another specific class of markers includes the so-called functional markers (FMs), which are supposed to have a cause and effect relationship with the traits of interest. In this review, we first describe the development of these markers representing the transcriptome or genes per se; we then discuss the uses of these markers in some detail and finally add a note on the future directions of research and the implications of the wider application of these markers in crop improvement programmes. Using suitable examples, we describe markers of different classes derived from cDNA clones, expressed sequence tags (ESTs), gene sequences and the unique (coding) sequences obtained through methyl filtration or genome normalization (high C(0) t fraction) from gDNA libraries. While we briefly describe RFLPs, SSRs, AFLPs and SNPs developed from the transcriptome (cDNA clones and EST databases), we have discussed in more detail some of the novel markers developed from the transcriptome and specific genes. These novel markers include expressed sequence tag polymorphisms (ESTPs), conserved orthologue set (COS) markers, amplified consensus genetic markers (ACGMs), gene specific tags (GSTs), resistance gene analogues (RGAs) and exon-retrotransposon amplification polymorphism (ERAP). Uses of these markers have been discussed in some detail under the following headings: development of transcript and functional maps, estimations of genetic diversity, marker-assisted selection (MAS), candidate-gene (CG) approach and map-based cloning, genetical genomics and identification of eQTLs, study of genome organization and taxonomic and phylogenetic studies. At the end, we also append a list of websites relevant to further studies on the transcriptome. For want of space, considerable information including voluminous data in the form of 12 tables, and a long list of references cited in these tables, has been placed on the Internet as electronic supplementary material (ESM), which the readers may find useful.

Chromosome Mapping↗
PubMed2026

Proteomic analysis of pancreatic endocrine cells by mechanistic single-cell isolation identifies membrane pathways.

To better understand diabetes and normoglycemia, pancreatic islet biology requires a precise molecular understanding of islet cell types at both the transcriptomic and proteomic levels. While transcriptomic analyses are well established, comprehensive proteomic characterization has been lacking, limiting our knowledge of islet molecular complexity. Here we introduce a nonenzymatic, mechanistic single-cell isolation technology using laser microdissection (LMD7), facilitating proteomic and transcriptomic analysis of physically isolated &#x3b1;-, &#x3b2;- and &#x3b4;-cells from fresh-frozen, unfixed pancreatic tissue. This mechanistic approach avoids enzymatic digestion and chemical fixation, preserving the cells' native molecular state before processing. Given the limited existing proteomic data, we supplemented our findings with transcriptomic analysis generated using the same method and compared our results with data from enzymatically isolated cells, obtained by fluorescence-activated cell sorting and compiled by others. Our analysis revealed that enzymatic digestion alters gene expression patterns, particularly those of membrane-associated proteins, underscoring the impact of isolation techniques on biological outcomes. We identified cell-type-specific proteins typically underrepresented in pancreatic single-cell transcriptomic datasets. &#x3b2;-cells exhibited enrichment in vesicle trafficking proteins, &#x3b1;-cells displayed distinct calcium-dependent action potential machinery and &#x3b4;-cells showed elevated expression of focal adhesion-related proteins. In addition, we report an inverse molecular relationship between &#x3b2;- and &#x3b4;-cells, potentially driven by transcriptional regulators such as Mlxipl. By establishing robust molecular profiles directly from intact pancreatic tissue, this work provides a reference point for future pathological comparisons, offering a framework to investigate how diabetes and other endocrine disorders reshape islet cell biology.

Journal Article↗
PubMed2003

Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes.

Platelets, while anucleate, contain RNA, some of which is translated into protein upon activation. Hypothesising that the platelet proteome is reflected in the transcriptome, we identified 82 proteins secreted from activated platelets and compared these, as well as published proteomic data, to the transcriptional profile. We also compared the transcriptome of platelets to other tissues to identify platelet-specific genes and used ontology to determine gene categories over-represented in platelets. RNA was isolated from highly pure platelet preparations for hybridization to Affymetrix oligonucleotide arrays. We identified 2,928 distinct messages as being present in platelets. The platelet transcriptome was compared with the proteome by relating both to UniGene clusters. Platelet proteomic data correlated well with the transcriptome, with 69% of secreted proteins detectable at the mRNA level, and similar concordance was obtained using two published datasets. While many of the most abundant mRNAs are for known platelet proteins, messages were detected for proteins not previously reported in platelets. Some of these may represent residual megakaryocyte messages; however, proteomic analysis confirmed the expression of many previously unreported genes in platelets. Transcripts for well-described platelet proteins are among the most platelet-specific messages. Ontological categories related to signal transduction, receptors, ion channels, and membranes are over-represented in platelets, while categories involved in protein synthesis are depleted. Despite the absence of gene transcription, the platelet proteome is mirrored in the transcriptome. Conversely, transcriptional analysis predicts the presence of novel proteins in the platelet. Transcriptional analysis is relevant to platelet biology, providing insights into platelet function and the mechanisms of platelet disorders.

Blood Platelets↗
PubMed2006

The functional characterization of normal and neoplastic human enterochromaffin cells.

CONTEXT: Neuroendocrine regulation of small intestinal (SI) function is poorly understood because pure neuroendocrine cells are unavailable, whereas the biological basis of SI carcinoid tumors is unknown because neoplastic human enterochromaffin (EC) cells are unavailable. OBJECTIVE: The objective of this study was to define the secretory regulation and transcriptome of naive and neoplastic SI neuroendocrine cells. DESIGN: EC cells from human ilea were isolated and purified, and a malignant EC cell carcinoid cell line (KRJ-I) was characterized. METHODS: Human ilea from right hemicolectomies were pronase/collagenase digested and Nycodenz gradient centrifuged, and EC cells were fluorescence-activated cell sorting (FACS) sorted after acridine orange labeling. Enrichment was defined by immunostaining, gene expression, serotonin (5-HT) content, and real-time RT-PCR. Naive FACS-sorted EC and KRJ-I cells were cultured, and 5-HT secretion was measured after stimulation with forskolin, isoproterenol, acetylcholine, gamma-aminobutyric acid A (GABA(A)), pituitary adenylate cyclase-activating polypeptide (PACAP)-38, and gastrin. Normal and neoplastic EC cell transcriptomes were acquired by Affymetrix profiling (U133A). RESULTS: FACS produced 100 +/- 0.3% (chromogranin A staining) and 99 +/- 0.7% pure EC cells by immunostaining for tryptophan hydroxylase with greater than 67-fold enrichment and a 5-HT content of 180 +/- 18 ng/mg protein (mucosa, 3.5 +/- 0.9). Forskolin- and isoproterenol-stimulated 5-HT secretion was 10-100 times more potent for naive cells (EC(50), 1.8 x 10(-9) m; 5.1 x 10(-9) m) than neoplastic cells (EC(50), 2.1 x 10(-7) m; 8.1 x 10(-8) m), but the effect of PACAP-38 was similar (EC(50), 1 x 10(-7) m). Isoproterenol stimulated cAMP levels 1.6 +/- 0.1-fold vs. basal (EC(50), 2.7 x 10(-9) m). Acetylcholine inhibited naive EC cell 5-HT secretion more potently than neoplastic (IC(50), 3.2 x 10(-9) vs. 1.6 x 10(-7) m), whereas GABA(A) was more potent in neoplastic cells (IC(50), 3.9 x 10(-10) vs. 4.4 x 10(-9) m). Octreotide inhibited naive, but not neoplastic, basal 5-HT secretion. Gastrin had no effect on 5-HT secretion. Comparison of naive and neoplastic transcriptomes revealed shared neuroendocrine and EC cell-specific marker genes. Real-time PCR confirmed that expression of adrenergic (beta1), somatostatinergic (SST(R)2), and neural (VPAC(1) and GABA(A)) receptors occurred on both cell types, but PACAP type 1 (PAC(1)) and cholecystokinin type 2 (CCK(2)) were undetectable. The putative carcinoid malignancy genes (MTA1 and MAGE-D2) were unique to the neoplastic EC cell transcriptome. CONCLUSION: These data support novel methodology to purify live human EC cells for functional characterization and transcriptome assessment, which will allow identification of new targets to control the secretion and proliferation of SI carcinoids.

Carcinoid Tumor↗
PubMed2006

Genome-wide analysis of group a streptococci reveals a mutation that modulates global phenotype and disease specificity.

Many human pathogens produce phenotypic variants as a means to circumvent the host immune system and enhance survival and, as a potential consequence, exhibit increased virulence. For example, it has been known for almost 90 y that clinical isolates of the human bacterial pathogen group A streptococci (GAS) have extensive phenotypic heterogeneity linked to variation in virulence. However, the complete underlying molecular mechanism(s) have not been defined. Expression microarray analysis of nine clinical isolates identified two fundamentally different transcriptomes, designated pharyngeal transcriptome profile (PTP) and invasive transcriptome profile (ITP). PTP and ITP GAS differed in approximately 10% of the transcriptome, including at least 23 proven or putative virulence factor genes. ITP organisms were recovered from skin lesions of mice infected subcutaneously with PTP GAS and were significantly more able to survive phagocytosis and killing by human polymorphonuclear leukocytes. Complete genome resequencing of a mouse-derived ITP GAS revealed that the organism differed from its precursor by only a 7-bp frameshift mutation in the gene (covS) encoding the sensor kinase component of a two-component signal transduction system implicated in virulence. Genetic complementation, and sequence analysis of covR/S in 42 GAS isolates confirmed the central role of covR/S in transcriptome, exoproteome, and virulence modulation. Genome-wide analysis provides a heretofore unattained understanding of phenotypic variation and disease specificity in microbial pathogens, resulting in new avenues for vaccine and therapeutics research.

Journal Article↗
PubMed2004

Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction.

Polymerase chain reaction (PCR)-based assays can target either DNA (the genome) or RNA (the transcriptome). Targeting the genome generates robust data that are informative and, most importantly, generally applicable. This is because the information contained within the genome is context-independent; i.e., generally, every normal cell contains the same DNA sequence--the same mutations and polymorphisms. The transcriptome, on the other hand, is context-dependent; i.e., the mRNA complement and level varies with physiology, pathology, or development. This makes the information contained within the transcriptome intrinsically flexible and variable. If this variability is combined with the technical limitations inherent in any reverse-transcription (RT)-PCR assay, it can be difficult to achieve not just a technically accurate but a biologically relevant result. Template quality, operator variability, the RT step itself, and subjectivity in data analysis and reporting are just a few technical aspects that make real-time RT-PCR appear to be a fragile assay that makes accurate data interpretation difficult. There can be little doubt that in the future, transcriptome-based analysis will become a routine technique. However, for the time being it remains a research tool, and it is important to recognize the considerable pitfalls associated with transcriptome analysis, with the successful application of RTPCR depending on careful experimental design, application, and validation.

Animals↗
PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir&#x2003;e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗
PubMed2026

Optics-free spatial genomics for mapping mammalian brain aging by IRISeq.

Spatial transcriptomics has emerged as a transformative approach for in situ mapping of cellular heterogeneity and interactions, yet existing methods often compromise throughput, cost and tissue coverage. Here we introduce Imaging Reconstruction using Indexed Sequencing (IRISeq): an optics-free, cost-effective platform that leverages spatial interaction mapping by indexed sequencing to profile tissues at adjustable sizes and resolutions (5-50&#x2009;&#xb5;m). We applied IRISeq to map gene expression across more than 70 coronal sections from both adult and aged mouse brains, including wild-type and two lymphocyte-deficient models (Rag1 and Prkdc mutants) and generated more than 460,000 spatial transcriptome profiles. Our integrated analysis with 783,264 single-cell transcriptomes revealed region-specific aging signatures that are lymphocyte dependent, notably a downregulation of interferon signaling and inflammation in ventricular regions upon lymphocyte depletion, alongside mutant-specific upregulation of senescence pathways. Furthermore, lymphocyte deficiency was linked to preserved abundance of ependymal cells that line the brain's ventricles and to distinct microglial state dynamics, highlighting a key role for lymphocytes in driving inflammatory processes during brain aging. Overall, IRISeq provides a high-throughput and cost-effective solution for spatially resolved transcriptomic profiling, opening new avenues for elucidating region-specific cellular mechanisms underlying aging and identifying potential therapeutic targets to preserve brain homeostasis.

Animals↗
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&#x2009;=&#x2009;91), with fewer studies examining samples from individuals of advanced reproductive age (n&#x2009;=&#x2009;45) and fetal (n&#x2009;=&#x2009;16) samples. Transcriptome analyses were most common (n&#x2009;=&#x2009;103, 85%), followed by proteome (n&#x2009;=&#x2009;19, 16%) and epigenome (n&#x2009;=&#x2009;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↗