Tissue Types Research Articles

#2485 CellMatchR—RNA-seq based matching of kidney cell lines to kidney reference cell types

Abstract Background and Aims Cultured kidney cell lines are broadly used to model the physiology and pathophysiology of the kidney. Due to immortalization, passaging and culture conditions, a cell line's gene expression profile might differ greatly from the primary cells it was originally derived from. This might be further enhanced by experimental treatments with compounds or genetic modifications. It is therefore a relevant question if a given cell culture system is still the appropriate model for a certain disease or scientific investigations. Reasoning that RNA sequencing (RNA-seq) based transcriptomes are generated as part of many experimental setups and hence may be used to address this question, we developed and tested an RNA-seq based approach, CellMatchR. This approach matches kidney cell lines, primary cells and even tissue specimen to known kidney reference cell types to determine which primary kidney cell type is the most similar and to what degree global gene expression or the expression of selected marker genes differs. Method CellMatchR uses published murine and human kidney single cell- and tubule-level transcriptomic datasets from healthy mice and human donors as references. Single cell datasets were further processed to pseudobulk references for each of the contained cell types. Then RNA-seq data from cell lines or tissues of interest (test data) was compared to the reference (pseudo)bulk data with Spearman correlations of gene counts-per-million values (CPM) or Euclidean distance using log-transformed CPM values. Both approaches were systematically tested for various combinations of test and reference datasets across different species and using global gene expression (i.e. all genes measured in reference and test datasets) and a set of 315 manually curated, kidney cell type marker genes. Results We sequentially used different kidney tissue types, primary cells and cell lines as positive controls and matched them to our reference datasets. Spearman correlations of gene expression rankings showed the highest correlation coefficients with biologically correct reference cell types (examples in Fig. 1A-C). We found Spearman correlations to be superior to Euclidean distances as method of comparison. Analyses based on global gene expression compared to our curated set of 315 kidney cell type marker genes yielded similar results. Notably, correlation coefficients were higher and showed less variation between reference cell types when using the global gene set. Matchings across species, i.e. using murine test and human reference data or vice versa still yielded mostly correct results. However, correlation coefficients were generally lower (i.e. rho = 0.9 vs. 0.6) and varied more across reference datasets if comparisons were performed across species. Using published RNA-seq data for different cell lines of the proximal tubule (e.g. human HK-2 and opossum OKH cells) we observed low similarities with proximal tubule cells (Fig. 1D), whereas two tested cell lines of the collecting duct (mIMCD-3 and mpkCCD cells) plausibly showed the highest similarity to medullary cell types like collecting duct and Loop of Henle cells. The most similar reference cell types did not change in mIMCD-3 cells when kept in 2-dimensional vs. 3-dimensional culture conditions, and in mpkCCD cells when the osmolality of the culture medium was changed from 300 to 600 mosmol/kg. Also, a Pkd1 knockout in mIMCD-3 cells did not change the most similar reference cell type in our analyses (Fig. 1E and F). Conclusion Our CellMatchR approach uses publicly available kidney single cell and bulk RNA-seq datasets and combines these with simple, computationally fast yet effective statistical methods to determine similarities of kidney cell lines and tissue samples to reference cell types of interest. It can easily be implemented by trained users or integrated into online resources for usage with a visual interface. It relies on RNA-seq data from the cell lines of interest and hence presents a feasible complementary method to check the general similarity of cell culture models to kidney cell types and assess their stability across experimental conditions.

The ciliary protein C2cd3 is required for mandibular musculoskeletal tissue patterning

The mandible is composed of several musculoskeletal tissues including bone, cartilage, and tendon that require precise patterning to ensure structural and functional integrity. Interestingly, most of these tissues are derived from one multipotent cell population called cranial neural crest cells (CNCCs). How CNCCs are properly instructed to differentiate into various tissue types remains nebulous. To better understand the mechanisms necessary for the patterning of mandibular musculoskeletal tissues we utilized the avian mutant talpid2 (ta2) which presents with several malformations of the facial skeleton including dysplastic tendons, mispatterned musculature, and bilateral ectopic cartilaginous processes extending off Meckel's cartilage. We found an ectopic epithelial BMP signaling domain in the ta2 mandibular prominence (MNP) that correlated with the subsequent expansion of SOX9+ cartilage precursors. These findings were validated with conditional murine models suggesting an evolutionarily conserved mechanism for CNCC-derived musculoskeletal patterning. Collectively, these data support a model in which cilia are required to define epithelial signal centers essential for proper musculoskeletal patterning of CNCC-derived mesenchyme.

Bioinformatics study of the potential therapeutic effects of ginsenoside Rh3 in reversing insulin resistance

BackgroundIn recent years, the incidence of insulin resistance is increasing, and it can cause a variety of Metabolic syndrome. Ginsenosides have been clinically proven to improve fat metabolism and reduce insulin resistance, but their components and mechanism of action are still unclear.ObjectiveGinsenoside, a bioactive compound derived from ginseng, exhibits significant potential in treating obesity, diabetes, and metabolic disorders. Despite evidence supporting its efficacy in ameliorating insulin resistance (IR) in obesity, the specific bioactive components and underlying mechanisms remain obscure. In this study, we endeavored to elucidate the potential molecular targets and pathways influenced by ginsenoside Rh3 (GRh3) to ameliorate IR in liver tissue. We employed a comprehensive approach that integrates system pharmacology and bioinformatics analysis.Materials and methodsOur methodology involved the identification of candidate targets for GRh3 and the profiling of differentially expressed genes (DEGs) related to IR in individuals with insulin resistance. The coalescence of candidate targets and DEGs facilitated the construction of a “GRh3-targets-disease” network for each tissue type, ultimately yielding 38 shared target genes. Subsequently, we conducted pathway enrichment analysis, established protein-protein interaction (PPI) networks, and identified hub targets among the GRh3 targets and IR-related DEGs. Additionally, we conducted animal experiments to corroborate the role of these hub targets in the context of GRh3.ResultsOur investigation identified a total of 38 overlapping targets as potential candidates. Notably, our analysis revealed crucial hub targets such as EGFR, SRC, ESR1, MAPK1, and CASP3, alongside implicated signaling pathways, including those related to insulin resistance, the FoxO signaling pathway, the PPAR signaling pathway, and the IL-17 signaling pathway. This study establishes a robust foundation for the mechanisms underlying GRh3’s efficacy in mitigating IR. Furthermore, these results suggest that GRh3 may serve as a representative compound within the ginsenoside family.ConclusionThis study elucidates the potential molecular targets and associated pathways through which GRh3 ameliorates IR, showcasing its multifaceted nature, spanning multiple targets, pathways, and mechanisms. These findings establish a robust foundation for subsequent experimental inquiries and clinical applications.

Vertical distribution and tissue selection of Helicoverpa zea (Lepidoptera: Noctuidae) adult oviposition and neonates on soybean with an indeterminate or determinate growth habit.

Helicoverpa zea (Boddie) is a polyphagous pest that can cause serious damage to crops, including soybeans (Glycine max L.). In soybeans with both determinate and indeterminate growth habits, H. zea larvae are more commonly found on leaves compared to blooms, stems, and pods. Past research demonstrated that H. zea adults tend to oviposit near the upper part of the plant canopy in soybeans with a determinate growth habit. However, ovipositional selection on soybeans with an indeterminate growth habit is unknown. We hypothesized that H. zea would oviposit more uniformly throughout the canopy on indeterminate soybean growth habits due to more diffuse reproductive tissue. We planted field and greenhouse experiments with varieties sharing a similar relative maturity (2 maturity group [MG] 5.2 varieties and 2 MG 5.4/5.5 varieties) but with different growth habits. To test oviposition selection, adult H. zea females were allowed to oviposit under field and caged conditions. We counted the number of H. zea eggs and neonates on each plant tissue type from each of 3 equal parts in the plant canopy: upper, middle, and lower. In both experiments, eggs and neonates were most common on leaves at the top of the plant regardless of soybean growth habit. Consequently, ovipositional selection is likely independent of reproductive tissue availability, and patterns of oviposition through the canopy are similar in growth habits. An improved understanding of H. zea ecology in soybeans relative to indeterminate growth habits may improve recommendations for managing this significant pest of soybean.

A whole-slide foundation model for digital pathology from real-world data

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1–3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision–language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

Cellular Transformation by Human Cytomegalovirus.

Epstein-Barr virus (EBV), Kaposi sarcoma human virus (KSHV), human papillomavirus (HPV), hepatitis B and C viruses (HBV, HCV), human T-lymphotropic virus-1 (HTLV-1), and Merkel cell polyomavirus (MCPyV) are the seven human oncoviruses reported so far. While traditionally viewed as a benign virus causing mild symptoms in healthy individuals, human cytomegalovirus (HCMV) has been recently implicated in the pathogenesis of various cancers, spanning a wide range of tissue types and malignancies. This perspective article defines the biological criteria that characterize the oncogenic role of HCMV and based on new findings underlines a critical role for HCMV in cellular transformation and modeling the tumor microenvironment as already reported for the other human oncoviruses.

Exploring Alashan Ground Squirrel (Spermophilus alashanicus) Diversity: Metagenomic and Transcriptomic Datasets from the Helan Mountains

This study investigates the adaptive strategies of the Alashan Ground Squirrel (Spermophilus alashanicus) in response to habitat changes, as rodents are sensitive indicators of ecosystem changes. Despite its ecological importance, the genome and microbiome of this species have not been thoroughly studied. This research fills this gap by presenting the first comprehensive metagenomic and transcriptomic datasets of the species. Transcriptomic data was collected from five tissue types, including heart, liver, cecum, muscle, and blood, resulting in the assembly of 72,156 unigenes. Metagenomic sequencing identified predominant bacterial groups such as Firmicutes, Bacteroidetes, Verrucomicrobia, Urovircota, and Proteobacteria. Our workflow involved RNA and DNA extraction, library preparation, assembly, and annotation, yielding valuable insights into gene discovery, microbial composition, and further genome and microbial function studies. In conclusion, our findings have significant implications for understanding the adaptive mechanisms of this species in response to environmental changes.

A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease.

The role of the extracellular matrix (ECM) in Parkinson's disease (PD) is not well understood, even though it is critical for neuronal structure and signaling. This systematic review identified the top deregulated ECM-related pathways in studies that used gene set enrichment analyses (GSEA) to document transcriptomic, proteomic, or genomic alterations in PD. PubMed and Google scholar were searched for transcriptomics, proteomics, or genomics studies that employed GSEA on data from PD tissues or cells and reported ECM-related pathways among the top-10 most enriched versus controls. Twenty-seven studies were included, two of which used multiple omics analyses. Transcriptomics and proteomics studies were conducted on a variety of tissue and cell types. Of the 17 transcriptomics studies (16 data sets), 13 identified one or more adhesion pathways in the top-10 deregulated gene sets or pathways, primarily related to cell adhesion and focal adhesion. Among the 8 proteomics studies, 5 identified altered overarching ECM gene sets or pathways among the top 10. Among the 4 genomics studies, 3 identified focal adhesion pathways among the top 10. The findings summarized here suggest that ECM organization/structure and cell adhesion (particularly focal adhesion) are altered in PD and should be the focus of future studies.

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the possibility that hyperspectral imaging could become a valuable tool in solving this problem. Hyperspectral images of 11 histologically diagnosed carcinomas (six basal cell carcinomas and five squamous cell carcinomas) were acquired prior clinical evaluation and surgical excision. The hyperspectral data were then analyzed using a newly developed method for delineating skin cancer tumor margins. This proposed method is based on a segmentation process of the hyperspectral images into regions with similar spectral and spatial features, followed by a machine learning-based data classification process resulting in the generation of classification maps illustrating tumor margins. The Spectral Angle Mapper classifier was used in the data classification process using approximately 37% of the segments as the training sample, the rest being used for testing. The receiver operating characteristic was used as the method for evaluating the performance of the proposed method and the area under the curve as a metric. The results revealed that the performance of the method was very good, with median AUC values of 0.8014 for SCCs, 0.8924 for BCCs, and 0.8930 for normal skin. With AUC values above 0.89 for all types of tissue, the method was considered to have performed very well. In conclusion, hyperspectral imaging can become an objective aid in the preoperative evaluation of carcinoma margins.

Cysteine S-acetylation is a post-translational modification involved in metabolic regulation.

Cysteine is a reactive amino acid central to the catalytic activities of many enzymes. It is also a common target of post-translational modifications (PTMs), such as palmitoylation. This longchain acyl PTM can modify cysteine residues and induce changes in protein subcellular localization. We hypothesized that cysteine could also be modified by short-chain acyl groups, such as cysteine S-acetylation. To test this, we developed sample preparation and non-targeted mass spectrometry protocols to analyze the mouse liver proteome for cysteine acetylation. Our findings revealed hundreds of sites of cysteine acetylation across multiple tissue types, revealing a previously uncharacterized cysteine acetylome. Cysteine acetylation shows a marked cytoplasmic subcellular localization signature, with tissue-specific acetylome patterns and specific changes upon metabolic stress. This study uncovers a novel aspect of cysteine biochemistry, highlighting short-chain modifications alongside known long-chain acyl PTMs. These findings enrich our understanding of the landscape of acyl modifications and suggest new research directions in enzyme activity regulation and cellular signaling in metabolism.

Identification of a New Domestic Pig Cell Line for Growth of African Swine Fever Viruses for Vaccine Production in Uganda.

In this study, a basis for the feasibility of a successful ASF vaccine design program using live attenuated vaccines was sought. One of the challenges to African swine fever (ASF) vaccine development is having a cell line that will provide commercial utility for vaccine production. We set out to address this problem by innovatively identifying possible cell lines from local domestic pigs in the country. Eight tissue types from ASF-negative animals were identified for incorporation into cell line development. These were degraded, incubated, and monitored for cell growth. One cell line, the SIR2-P, grew consistently and confluently and was tested for the ability to grow and isolate field ASF viruses. We report the isolation of ASF viruses in our laboratory for the first time. The P9C virus from Namayingo district exhibited the largest plaque sizes compared to the SQ517B virus from Mukono. The innovative identification of the SIR2-P cell line is proof of concept that newer speciesspecific cell lines can be developed in the Infectious Animal Disease Laboratory (IADL), and utilized to study other animal viruses like Porcine Reproductive and Respiratory Syndrome (PRRS) virus, Foot and mouth disease (FMD) virus, and Swine Influenza. Such cells can be used in vaccine production for other endemic diseases in Uganda. The SIR2-P cell line is currently at passage 65.

Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks.

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.

Lipomas of the oral cavity

INTRODUCTION. Lipomas are benign mesenchymal tumors consisting of mature adipocytes. They are classified according to their histological picture, localization in the oral cavity and the type of tissues involved in the pathological process.The OBJECTIVE was to present the clinical case and literature review.MATERIALS AND METHODS. We conducted the search in the PubMed database for the last 86 years (1937–2023) with keywords «oral lipoma» or «tongue lipoma».RESULTS. We reviewed 60 articles with 77 identified lipomas. The most publications were represented by the USA (n=10, 16.39%) and India (n=10, 16.39%). We found out that the average age was 51.38 years (51.38±15.57); and most of them occurred over 50 years old. Lipomas are mainly localized on the mucous membrane of the cheek (n=24, 31.17%), then on the tongue (n=17, 22.8%) and in other places. The most common histological picture was spindle cell lipomas (n=13, 27.66%), followed by fibrolipomas (n=8, 17.02%) and other types.CONCLUSIONS. Lipomas are relatively rare neoplasms of the oral cavity. chronic traumatization of the oral mucosa is the predisposing factor for lipoma development. Preventive examinations every six months is recommended, if a patient has dental constructions. Any formation removed from the oral cavity is subjected to histological verification.

Breast Glandular and Ductal Volume Changes during the Menstrual Cycle: A Study in 48 Breasts Using Ultralow-Frequency Transmitted Ultrasound Tomography/Volography.

The aim of this study was to show for the first time that low-frequency 3D-transmitted ultrasound tomography (3D UT, volography) can differentiate breast tissue types using tissue properties, accurately measure glandular and ductal volumes in vivo, and measure variation over time. Data were collected for 400 QT breast scans on 24 women (ages 18-71), including four (4) postmenopausal subjects, 6-10 times over 2+ months of observation. The date of onset of menopause was noted, and the cases were further subdivided into three (3) classes: pre-, post-, and peri-menopausal. The ducts and glands were segmented using breast speed of sound, attenuation, and reflectivity images and followed over several menstrual cycles. The coefficient of variation (CoV) for glandular tissue in premenopausal women was significantly larger than for postmenopausal women, whereas this is not true for the ductal CoV. The glandular standard deviation (SD) is significantly larger in premenopausal women vs. postmenopausal women, whereas this is not true for ductal tissue. We conclude that ducts do not appreciably change over the menstrual cycle in either pre- or post-menopausal subjects, whereas glands change significantly over the cycle in pre-menopausal women, and 3D UT can differentiate ducts from glands in vivo.

Evaluation of DNA yield from various tissue and sampling sources for use in single nucleotide polymorphism panels

Genetics studies are used by wildlife managers and researchers to gain inference into a population of a species of interest. To gain these insights, microsatellites have been the primary method; however, there currently is a shift from microsatellites to single nucleotide polymorphisms (SNPs). With the different DNA requirements between microsatellites and SNPs, an investigation into which samples can provide adequate DNA yield is warranted. Using samples that were collected from previous genetic projects from regions in the USA from 2014 to 2021, we investigated the DNA yield of eight sample categories to gain insights into which provided adequate DNA to be used in ddRADseq or already developed high- or medium-density SNP panels. We found seven sample categories that met the DNA requirements for use in all three panels, and one sample category that did not meet any of the three panels requirements; however, DNA integrity was highly variable and not all sample categories that met panel DNA requirements could be considered high quality DNA. Additionally, we used linear random-effects models to determine which covariates would have the greatest influence on DNA yield. We determined that all covariates (tissue type, storage method, preservative, DNA quality, time until DNA extraction and time after DNA extraction) could influence DNA yield.

PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.

Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). Methods: PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for βII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility. Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to 98%-100% for all users and across all samples. When PCPA's measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

Histocompatibility Testing: A Fundamental Aspect of Renal Transplant Workup

Histocompatibility testing is pivotal in any renal transplantation workup, aimed at enhancing prospective donor recipient compatibility and improving transplant outcomes. The evolution and advancement of histocompatibility testing, particularly HLA typing, have significantly improved its precision. This study outlines the historical progression from serologic to DNA-based HLA typing, emphasizing the role of HLA proteins in immune response. Anti-HLA antibodies, targeting HLA proteins, pose challenges in renal transplantation. Monitoring and managing these antibodies are critical for renal transplant success. Complement-dependent cytotoxicity crossmatch and flow cytometry crossmatch are essential techniques for assessing donor–recipient compatibility. Panel-reactive antibody assesses antibodies against a panel of donor antigens, often HLA. Higher PRA levels (percentage) complicate donor matching, requiring specialized protocols. Virtual crossmatch evaluates recipient anti-HLA antibodies against potential donors through synthetic beads. This approach predicts crossmatch outcomes by comparing antibody profiles, offering a valuable tool for the risk assessment of renal transplantation. Despite advancements, a comprehensive understanding of alloreactive immune responses requires a combination of assays, emphasizing the importance of a multifaceted approach in histocompatibility testing. This is an attempt to compile the relevant information, providing a basis for comparison in a clear and foundational format for histocompatibility testing laboratories.

Characterizing diseases using genetic and clinical variables: A data analytics approach

AbstractPredictive analytics is crucial in precision medicine for personalized patient care. To aid in precision medicine, this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types. To achieve this, experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables. In this study, the k‐means technique was used for clustering the diseased tissue types, and the multinomial logistic regression (MLR) technique was applied for classifying the diseased tissue types. Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables. The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non‐landmark genes, and the difference is statistically significant. Furthermore, it was evident that both clinical and genetic variables were important in predicting the diseased tissue types. The top three clinical predictors for predicting diseased tissue types were identified as morphology, gender, and age of diagnosis. Additionally, this study explored the possibility of using the latent representations of the clusters of landmark and non‐landmark genes as predictors for an MLR classifier. The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables. This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine, enhancing diagnostic accuracy.

Spatial detection of mitochondrial DNA and RNA in tissues.

Mitochondrial health has gained attention in a number of diseases, both as an indicator of disease state and as a potential therapeutic target. The quality and amount of mitochondrial DNA (mtDNA) and RNA (mtRNA) can be important indicators of mitochondrial and cell health, but are difficult to measure in complex tissues. mtDNA and mtRNA in zebrafish retina samples were fluorescently labeled using RNAscope™ in situ hybridization, then mitochondria were stained using immunohistochemistry. Pretreatment with RNase was used for validation. Confocal images were collected and analyzed, and relative amounts of mtDNA and mtRNA were reported. Findings regarding mtDNA were confirmed using qPCR. Signals from probes detecting mtDNA and mtRNA were localized to mitochondria, and were differentially sensitive to RNase. This labeling strategy allows for quantification of relative mtDNA and mtRNA levels in individual cells. As a demonstration of the method in a complex tissue, single photoreceptors in zebrafish retina were analyzed for mtDNA and mtRNA content. An increase in mtRNA but not mtDNA coincides with proliferation of mitochondria at night in cones. A similar trend was measured in rods. Mitochondrial gene expression is an important component of cell adaptations to disease, stress, or aging. This method enables the study of mtDNA and mtRNA in single cells of an intact, complex tissue. The protocol presented here uses commercially-available tools, and is adaptable to a range of species and tissue types.