Biology subjects

Loher, P.

Publications and source records attributed to Loher, P..

10 recordsLinked to original sources

bioRxiv2026

The SEA-AD DREAM Challenge: Community benchmarking human and AI agent solutions for Alzheimer's disease neuropathology prediction from single-nucleus transcriptomics

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

neuroscience↗

bioRxiv2024

Comprehensive profiling of small RNAs and their changes and linkages to mRNAs in schizophrenia and bipolar disorder

We investigated small non-coding RNAs (sncRNAs) from the prefrontal cortex of 93 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) and 77 controls. We uncovered recurring complex sncRNA profiles, with 98% of all sncRNAs being accounted for by miRNA isoforms (60.6%), tRNA-derived fragments (17.8%), rRNA-derived fragments (11.4%), and Y RNA-derived fragments (8.3%). In SCZ, 15% of all sncRNAs exhibit statistically significant changes in their abundance. In BD, the fold changes (FCs) are highly correlated with those in SCZ but less acute. Non-templated nucleotide additions to the 3'-ends of many miRNA isoforms determine their FC independently of miRNA identity or genomic locus of origin. In both SCZ and BD, disease- and age-associated sncRNAs and mRNAs reveal accelerated aging. Co-expression modules between sncRNAs and mRNAs align with the polarities of SCZ changes and implicate sncRNAs in critical processes, including synaptic signaling, neurogenesis, memory, behavior, and cognition.

neuroscience↗

bioRxiv2024

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders

Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a subset of principal components to residualize from expression data by maximizing the enrichment of "reference pairs" among highly correlated RNA-RNA pairs. Unlike traditional machine learning metrics, this novel enrichment-based metric is specifically designed to evaluate correlation data and provides valuable RNA-level interpretability. CorrAdjust outperforms current state-of-the-art methods when evaluated on 25,063 human RNA-seq datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and the Geuvadis collection. In particular, CorrAdjust excels at integrating small RNA and mRNA sequencing data, significantly enhancing the enrichment of experimentally validated miRNA targets among negatively correlated miRNA-mRNA pairs. CorrAdjust, with accompanying documentation and tutorials, is available through GitHub at https://tju-cmc-org.github.io/CorrAdjust.

bioinformatics↗

bioRxiv2024

All ribosomal RNAs and 45S spacers from humans to worms are packed with organism-specific motifs whose other copies are found predominantly in numerous nervous system genes including many associated with human disorders

The nucleotide sequences of ribosomal RNAs (rRNAs) and the spacers of 45S are tuned to fulfill optimally their respective roles during ribosome biosynthesis and function. We report that these sequences satisfy additional genome-wide constraints in humans, mice, fruit flies, and worms. In all four organisms, the rRNAs and 45S spacers are densely packed with organism-specific nucleotide motifs with many additional identical copies throughout the genome. The human rRNAs and 45S spacers contain 1,723 motifs whose sequences are unique to the rRNAs/spacers of primates. These motifs have numerous additional exact intronic and exonic copies whose genomic placement is also unique to primates. Specific combinations of the motifs appear exclusively in 3,430 human nervous system and developmental genes, including 1,046 risk genes for autism, schizophrenia, and bipolar disorder. RNA/RNA crosslinking experiments show that the rRNA/spacer motifs are contact points for rRNA-mRNA and mRNA-mRNA heteroduplexes. RNA binding protein (RBP) assays show that these motifs are also in the binding sites of 113 RBPs. RNA sequencing reveals that rRNAs and spacers produce endogenous small non-coding RNAs (sncRNAs) that carry the same primate-specific motifs. Lastly, the motifs intergenic and intronic copies overlap 131 GWAS polymorphisms associated with neuropathologies (p-val<3.9e-12). The findings suggest that the motifs facilitate RNA/RNA and RBP/RNA interactions that are affected by polymorphisms and modulated by rRNA- and spacer-derived sncRNAs carrying the same motifs. Our study also genetically links for the first time rRNAs and 45S spacers to autism and other typically human neurological disorders. Mutation panels based on these motifs can lead to new molecular diagnostics for these disorders, whereas snRNAs carrying these motifs can serve as drugs or potential therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/627812v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@146feadorg.highwire.dtl.DTLVardef@1fa501corg.highwire.dtl.DTLVardef@a17f14org.highwire.dtl.DTLVardef@9e2053_HPS_FORMAT_FIGEXP M_FIG C_FIG The human ribosomal RNAs and the 45S spacers share primate-specific motifs with thousands of nervous system genes, including risk genes for autism and other neurological disorders. Similar links between rRNAs/spacers and nervous system genes exist in mice, fruit flies, and worms but are achieved through motifs that are unique to each organism. In humans, the motifs are at the contact points of endogenous gene-gene and rRNA-gene heteroduplexes, as well as the binding sites of RNA binding proteins (RBPs) in genes and rRNAs/spacers. Endogenous small RNAs carrying these shared motifs modulate these endogenous interactions and are disrupted by mutations at the motifs genomic copies. Mutation panels based on these motifs can lead to new diagnostics for autism and other disorders, while the small RNAs carrying the motifs can serve as drugs or potential therapeutic targets.

neuroscience↗

bioRxiv2022

Single-nucleotide Differences and Cell Type Decide the Subcellular Localization of miRNA Isoforms (isomiRs), tRNA-derived Fragments (tRFs) and rRNA-derived Fragments (rRFs)

BackgroundMicroRNAs (miRNAs) and their isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) represent ~95% of all short RNAs found in cells. All three types modulate mRNA and protein abundance and are dysregulated in diseases. Experimental studies to date assumed that the subcellular localization of these molecules is well understood and constant across cell types. ResultsWe investigated the localization of isomiRs, tRFs, and rRFs in biological replicates from three frequently-used model cell lines. In each case, we analyzed the contents of the nucleus, cytoplasm, whole mitochondrion, mitoplast, and the whole cell. We used a rigorous mathematical model to account for cross-fraction contamination and technical errors and adjusted abundances accordingly. We found that isomiRs, tRFs, and rRFs exhibit complex and unexpected patterns of subcellular localization. These patterns depend on the type of the RNA molecule, its exact sequence, and the cell type. Even for "sibling" RNAs from the same parental RNA whose sequences differ by only a few nucleotides, their subcellular localization depends on each siblings exact sequence and the cell type. ConclusionsPrevious studies of isomiRs, tRFs, and rRFs that used ectopic expression without accounting for isoforms may need to be re-evaluated. Future experiments with these molecules will need to distinguish among the multiple isoforms and account for the fact that each isoforms abundance and destination depend on its exact sequence and cell type. The findings additionally suggest the existence of an intracellular trafficking program for isomiRs, tRFs, and rRFs and, by extension, expanded roles for these molecules - both dimensions await characterization. To help design future experiments, we compiled a first-of-its-kind Atlas to catalogue the subcellular localization and abundance of 5,898 isomiRs, tRFs, and rRFs across three model cell lines. Results SummaryO_LIWe analyzed the distribution of microRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) in the{whitebullet} nucleus {whitebullet}cytoplasm {whitebullet}mitochondrion, and {whitebullet}mitoplast of biological replicates from three cell lines from the same tissue. C_LIO_LIWe corrected the measured abundances by accounting for cross-fraction contamination and technical errors through a rigorous mathematical model. C_LIO_LIOur analysis revealed complex localization patterns involving numerous isomiRs, tRFs, and rRFs. C_LIO_LIThe subcellular localization of these RNAs depends on their exact sequence and differs even for molecules that arise from the same parental miRNA, tRNA, or rRNA. C_LIO_LIFor a given RNA, its subcellular localization additionally depends on cell type. C_LIO_LIThe findings have implications for previous and future molecular studies of the function of isomiRs, tRFs, and rRFs. C_LIO_LIThe findings suggest the existence of a complex subcellular trafficking program, and hint at expanded functions for these RNA molecules that differ by compartment. C_LIO_LITo assist with the design of future experiments, we created a first-of-its-kind Atlas that catalogues the subcellular distribution and abundance of 5,898 isomiRs, tRFs, and rRFs across three cell lines. C_LI

molecular biology↗

bioRxiv2019

Machine Learning Approaches Identify Genes Containing Spatial Information from Single-Cell Transcriptomics Data.

MotivationWe participated in the DREAM Single Cell Transcriptomics Challenge. The challenges focus was two-fold; a) to identify the top 60, 40 and 20 genes that contain the most spatial information, and b) to reconstruct the 3-D arrangement of the D. melanogaster embryo using information from those genes.\n\nResultsWe developed two independent approaches, leveraging machine learning models from Lasso and Deep Neural Networks, that we successfully apply to high-dimensional single-cell sequencing data. Our methods allowed us to achieve top performance when compared to the ground truth. Among ~40 participating teams, the resulting solutions placed 10th, 6th, and 4th in the three DREAM sub-challenges #1, #2 and #3, respectively. Notably, for the Lasso approach we introduced a feature selection technique, Lasso-TopX, that allows a user to define a specific number of features they are interested in and the Neural Network approach utilizes weak supervision for linear regression to accommodate for uncertain or probabilistic training labels. Furthermore, we identified novel D. melanogaster genes that carry important positional information and were not previously suspected. Lastly, we show how the indirect use of the full datasets information can lead to data leakage and generate bias in overestimating the models performance.\n\nAvailabilityhttps://github.com/TJU-CMC-Org/SingleCell-DREAM/.\n\nContactNestoras.Karathanasis@jefferson.edu

bioinformatics↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗

bioRxiv2018

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

BackgroundMicroRNAs (miRNAs) are small RNA molecules ([~]22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, a lack of consensus on miRNA/isomiR analyses exists, and the resulting diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.\n\nFindingsTo overcome this situation, we present here a community-based project, miRTOP (miRNA Transcriptomic Open Project) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream analysis tools that are compatible with any existing detection and quantification tool. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, miRge2.0, isomiR-SEA, sRNAbench, and Prost!, as well as BAM files. Its open architecture enables any tool or pipeline to output results in mirGFF3.\n\nConclusionsCollectively a comprehensive isomiR categorization system, along with the accompanying mirGFF3 and mirtop API provide a complete solution for the standardization of miRNA and isomiR analysis, enabling data sharing, reporting, comparative analyses, and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps to miRNA detection, annotation, and quantification.

bioinformatics↗

bioRxiv2017

Systems-Level Analysis Of 32 TCGA Cancers Reveals Disease-Dependent tRNA Fragmentation Patterns And Very Selective Associations With Messenger RNAs And Repeat Elements

We mined 10,274 datasets from The Cancer Genome Atlas (TCGA) for tRNA fragments (tRFs) that overlap nuclear and mitochondrial (MT) mature tRNAs. Across 32 cancer types, we identified 20,722 distinct tRFs, a third of which arise from MT tRNAs. Most of the fragments belong to the novel category of i-tRFs, i.e. they are wholly internal to the mature tRNAs. The abundances and cleavage patterns of the identified tRFs depend strongly on cancer type. Of note, in all 32 cancer types, we find that tRNAHisGTG produces multiple and abundant 5{acute}-tRFs with a uracil at the -1 position, instead of the expected post-transcriptionally-added guanosine. Strikingly, these -1U His 5{acute}tRFs are produced in ratios that remain constant across all analyzed normal and cancer samples, a property that makes tRNAHisGTG unique among all tRNAs. We also found numerous tRFs to be negatively correlated with many messenger RNAs (mRNAs) that belong primarily to four universal biological processes: transcription, cell adhesion, chromatin organization and development/morphogenesis. However, the identities of the mRNAs that belong to these processes and are negatively correlated with tRFs differ from cancer to cancer. Notably, the protein products of these mRNAs localize to specific cellular compartments, and do so in a cancer-dependent manner. Moreover, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in multiple categories of repeat elements. Conversely, the genomic span of mRNAs that are positively correlated with tRFs are depleted in repeat elements. These findings suggest novel and far-reaching roles for tRFs and indicate their involvement in system-wide interconnections in the cell. All discovered tRFs from TCGA can be downloaded from https://cm.jefferson.edu/tcga-mintmap-profiles or studied interactively through the newly-designed version 2.0 of MINTbase at https://cm.jefferson.edu/MINTbase.\n\nNOTE: while the manuscript is under review, the content on the page https://cm.jefferson.edu/tcgamintmap-profiles is password protected and available only to Reviewers.\n\nKey PointsO_LIComplexity: tRNAs exhibit a complex fragmentation pattern into a multitude of tRFs that are conserved within the samples of a given cancer but differ across cancers.\nC_LIO_LIVery extensive mitochondrial contributions: the 22 tRNAs of the mitochondrion (MT) contribute 1/3rd of all tRFs found across cancers, a disproportionately high number compared to the tRFs from the 610 nuclear tRNAs.\nC_LIO_LIUridylated (not guanylated) 5{acute}-His tRFs: in all human tissues analyzed, tRNAHisGTG produces many abundant modified 5{acute}-tRFs with a U at their \"-1\" position (-1U 5{acute}-tRFs), instead of a G.\nC_LIO_LILikely central roles for tRNAHisGTG: the relative abundances of the -1U 5{acute}-tRFs from tRNAHisGTG remain strikingly conserved across the 32 cancers, a property that makes tRNAHisGTG unique among all tRNAs and isoacceptors.\nC_LIO_LISelective tRF-mRNA networks: tRFs are negatively correlated with mRNAs that differ characteristically from cancer to cancer.\nC_LIO_LIMitochondrion-encoded tRFs are associated with nuclear proteins: in nearly all cancers, and in a cancer-specific manner, tRFs produced by the 22 mitochondrial tRNAs are negatively correlated with mRNAs whose protein products localize to the nucleus.\nC_LIO_LItRFs are associated with membrane proteins: in all cancers, and in a cancer-specific manner, nucleus-encoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products localize to the cells membrane.\nC_LIO_LItRFs are associated with secreted proteins: in all cancers, and in a cancer-specific manner, nucleusencoded and MT-encoded tRFs are negatively correlated with mRNAs whose protein products are secreted from the cell.\nC_LIO_LItRFs are associated with numerous mRNAs through repeat elements: in all cancers, and in a cancerspecific manner, the genomic span of mRNAs that are negatively correlated with tRFs are enriched in specific categories of repeat elements.\nC_LIO_LIintra-cancer tRF networks can depend on sex and population origin: within a cancer, positive and negative tRF-tRF correlations can be modulated by patient attributes such as sex and population origin.\nC_LIO_LIweb-enabled exploration of an \"Atlas for tRFs\": we released a new version of MINTbase to provide users with the ability to study 26,531 tRFs compiled by mining 11,719 public datasets (TCGA and other sources).\nC_LI

systems biology↗

bioRxiv2016

The presence or absence alone of miRNA isoforms (isomiRs) successfully discriminate amongst the 32 TCGA cancer types

Previously, we demonstrated that miRNA isoforms (isomiRs) are constitutive and their expression profiles depend on tissue, tissue state, and disease subtype. We have now extended our isomiR studies to The Cancer Genome Atlas (TCGA) repository. Specifically, we studied whether isomiR profiles can distinguish amongst the 32 cancers. We analyzed 10,271 datasets from 32 cancers and found 7,466 isomiRs from 807 miRNA hairpin-arms to be expressed above threshold. Using the top 20% most abundant isomiRs, we built a classifier that relied on \"binary\" isomiR profiles: isomiRs were simply represented as present or absent and, unlike previous methods, all knowledge about their expression levels was ignored. The classifier could label tumor samples with an average sensitivity of 93% and a False Discovery Rate of 3%. Notably, its ability to classify well persisted even when we reduced the set of used features (=isomiRs) by a factor of 10. A counterintuitive finding of our analysis is that the isomiRs and miRNA loci with the highest ability to classify tumors are not the ones that have been attracting the most research attention in the miRNA field. Our results provide a framework in which to study cancer-type-specific isomiRs and explore their potential uses as cancer biomarkers

bioinformatics↗