Biology subjects

Robinson, J. L.

Publications and source records attributed to Robinson, J. L..

At least 19 recordsLinked to original sources

bioRxiv2025

Endogenous Estrogen-Mimetic Compounds in Cell Culture Media Influence Human Mesenchymal Stromal Cell (hMSC) Processes and Differentiation in a Sex-Biased Manner

Estrogens are global regulators of cellular signaling pathways, impacting fundamental processes and phenotypes that are essential for tissue remodeling and homeostasis. Traditional cell culture media contains estrogen-mimetic compounds, including phenol red and endogenous estrogen in fetal bovine serum (FBS). However, the potential of these compounds to bias in vitro studies, particularly when considering sex as a biological variable, remains unclear. This gap in understanding critically impacts the culture of human mesenchymal stromal cells (hMSCs), whose basic functions and differentiation potential, central to cell therapy and tissue engineering, are sensitive to perturbations in the culture conditions. Despite this, the effect of estrogens from cell culture media on male and female hMSCs is not currently considered in cell processing for clinical trials. As such, a baseline understanding of these estrogen-mimetic media influences on hMSCs is critical for clinical efficacy and adequate study design in research. To this end, we investigated the effects of phenol red and fetal bovine serum on the proliferation, metabolism, senescence, and differentiation capacity of male and female hMSCs. Phenol red, FBS, donor sex, and 17{beta}-estradiol (E2) supplementation all had significant impacts on hMSC health and differentiation potential in culture. Notably, dosing with estrogen at the levels found in FBS did not recover most of the hMSC metrics tested. The only outcomes that were not significantly different based on donor sex were senescence and mRNA transcripts for RUNX2 and PPARG, transcriptional regulators for osteogenesis and adipogenesis. Overall, these findings reveal the sex-biased effects of estrogen and estrogen-mimetic compounds in traditional culture media, underscoring a current gap in considering sex as a biological variable in cell therapy and tissue engineering research and manufacturing.

bioengineering↗

bioRxiv2025

Context-dependent Interactors Regulate TDP-43 Dysfunction in ALS/FTLD

TDP-43 mislocalization, aggregation, and loss of splicing function are neuropathological hallmarks in over 97% of Amyotrophic Lateral Sclerosis (ALS), 45% of Frontotemporal Lobar Degeneration (FTLD), and 60% of Alzheimers Disease, which has been reclassified as LATE-NC. However, the mechanisms underlying TDP-43 dysfunction remain elusive. Here, we utilize APEX2-driven proximity labeling and mass spectrometry to characterize the context-dependent TDP-43 interactome in conditions of cytoplasmic mislocalization, impaired RNA-binding contributing to aggregation, and oxidative stress. We describe context-dependent interactors, including disrupted interactions with splicing-related proteins and altered biomolecular condensate (BMC) associations. By integrating ALS and FTLD snRNA-seq data, we uncover disease-relevant molecular alterations and validate our dataset through a functional screen that identifies key TDP- 43 regulators. We demonstrate that disrupting nuclear speckle integrity, particularly through the downregulation of the splicing factor SRRM2, promotes TDP-43 mislocalization and loss of function. Additionally, we identify NUFIP2 as an interactor associated with mislocalization that sequesters TDP-43 into cytoplasmic aggregates and co-localizes with TDP-43 pathology in patient tissue. We also highlight HNRNPC as a potent TDP-43 splicing regulator, where precise modulation of TDP-43 or HNRNPC can rescue cryptic exon splicing. These findings provide mechanistic insights and potential therapeutic targets for TDP-43 dysfunction.

molecular biology↗

bioRxiv2024

Mandrel Diameter is a Dominating Parameter for Fiber Alignment Control in Rotating Mandrel Electrospinning Systems

Aligned nano and micron-sized electrospun scaffolds are advantageous for 3D in vitro models of fibrous, aligned tissue. A common approach to induce alignment is to collect on a rotating mandrel at high rotational speeds. Historically, rotating mandrel speed has been considered the major driver in tuning the degree of alignment even though mandrel diameter is known to modulate linear velocity and increase alignment. However, the comparative impact of mandrel diameter vs. rotating mandrel speed has not been systemically investigated. As such, this study aimed to investigate the role of mandrel diameter on fiber alignment, fiber fraction, and fiber diameter under controlled modulation of common processing parameters including applied voltage, distance to collector, and mandrel rotational speed. Analysis of all samples was performed using scanning electron microscopy (SEM) and image analysis by the DiameterJ and OrientationJ plugins in ImageJ. Using linear regression analysis in JMP software, mandrel diameter was shown to be the dominant factor influencing fiber diameter, fiber fraction, and fiber alignment of samples at all tested conditions including increased rotational speed. Overall, these findings suggest that rather than increasing rotational speed of the collector, fiber alignment can be more finely tuned by increasing mandrel diameter. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/603153v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@a493dorg.highwire.dtl.DTLVardef@14e7d42org.highwire.dtl.DTLVardef@1925eorg.highwire.dtl.DTLVardef@1308da3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Revealing Early Spatial Patterns of Cellular Responsivity in Fiber-Reinforced Microenvironments

Fiber-reinforcement approaches have been utilized to replace aligned tissues with engineered constructs after injury or surgical resection, strengthening soft biomaterial scaffolds and replicating anisotropic, load-bearing properties. However, most studies focus on the macroscale aspects of these scaffolds, rarely considering the cell-biomaterial interactions that govern remodeling and ECM organization towards aligned neo-tissues. Since initial cell-biomaterial responses within fiber-reinforced microenvironments likely influence long-term efficacy of repair and regeneration strategies, here we elucidate roles of spatial orientation, substrate stiffness, and matrix remodeling on early cell-fiber interactions. Bovine mesenchymal stromal cells (MSCs) were cultured in soft fibrin gels reinforced with a stiff 100 {micro}m polyglycolide-co-caprolactone fiber. Gel stiffness and remodeling capacity were modulated by fibrinogen concentration and aprotinin treatment, respectively. MSCs were imaged at 3 days and evaluated for morphology, mechanoresponsiveness (nuclear YAP localization), and spatial features including distance and angle deviation from fiber. Within these constructs, morphological conformity decreased as a function of distance from fiber. However, these correlations were weak (R2 = 0.01043 for conformity and R2 = 0.05542 for nuclear YAP localization), illustrating cellular heterogeneity within fiber-enforced microenvironments. To better assess cell-fiber interactions, we applied machine-learning strategies to our heterogeneous dataset of cell shape and mechanoresponsive parameters. Principal component analysis (PCA) was used to project 23 input parameters (not including distance) onto 5 principal components (PCs), followed by Agglomerative Hierarchical Clustering (AHC) to classify cells into 3 groups. These clusters exhibited distinct levels of morpho-mechanoresponse (combination of morphological conformity and YAP signaling) and were classified as High Response (HR), Medium Response (MR), and Low Response (LR) clusters. Cluster distribution varied spatially, with most cells (61%) closest to the fiber (0 - 75 {micro}m) belonging to the HR cluster, and most cells (55%) furthest from the fiber (225 - 300 {micro}m) belonging to the LR cluster. Modulation of gel stiffness and fibrin remodeling showed differential effects for HR cells, with stiffness influencing the level of mechanoresponse, and remodeling capacity influencing the location of responding cells. Overall, clustering of individual cells in stiff-soft microenvironments revealed spatial trends in cellular responsivity not seen by evaluating individual cell parameters as a distance from fiber alone. Impact StatementThis study used PCA-AHC based clustering to identify MSC sub-groups from a heterogeneous population with distinct responses to stiff-soft microenvironments. Cell responsivity within a soft, fiber-reinforced fibrin gel microenvironment was influenced by the spatial localization of individual cells around a stiffer polyglycolide-co-caprolactone fiber. Additionally, modulation of gel substrate stiffness and matrix remodeling capacity further influenced the level of responsiveness and localization of responsive cell clusters around the fiber, which may contribute to scaffold design at the cellular level and foreshadow longer-term aligned tissue deposition.

bioengineering↗

bioRxiv2023

Meta-analytic decoding of the cortical gradient of functional connectivity

Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

neuroscience↗

bioRxiv2023

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

bioRxiv2022

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport

Energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons. Here we used metabolic models that estimate costs of enzyme usage per pathway, enzyme utilization over time, and enzyme transport to evaluate a paradigm that suggests that brain energy metabolism is optimized to minimize enzyme synthesis and transportation costs. Our models recapitulate known metabolic behaviors and provide explanation for the astrocyte-neuron lactate shuttle theory.

systems biology↗

bioRxiv2022

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

systems biology↗

bioRxiv2022

Cellular limitation of enzymatic capacity explains glutamine addiction in cancers

The tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

cancer biology↗

bioRxiv2021

Denoising physiological data collected during multi-band, multi-echo EPI sequences

Collecting physiological data during fMRI experiments can improve fMRI data cleaning and contribute to our understanding of psychophysiological processes; however, these recordings are frequently fraught with artifacts from the MRI pulse sequence. Here, we assess data from BIOPAC Systems, Inc., one of the more widely-used manufacturers of physiological monitoring equipment, and evaluate their recommendations for filtering such artifacts from electrocardiogram and electrodermal activity data collected during single-band, single-echo fMRI sequences and extend these recommendations to address artifacts associated with multiband, multi-echo fMRI sequences. While the magnitude and frequencies of artifacts differ with these aspects of pulse sequences, their effects can be mitigated via application of digital filters incorporating slice collection, multiband factor, and repetition time. The implementation of these filters is provided both in interactive online notebooks and an open source denoising tool.

physiology↗

bioRxiv2021

3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe

Tau protein neurofibrillary tangles (NFT) are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimers disease (AD) and related dementias. Our knowledge of the pattern of NFT progression in the human brain, critical to the development of imaging biomarkers and interpretation of in vivo imaging studies in AD, is based on conventional 2D histology studies that only sample the brain sparsely. To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe (MTL) specimens were used to construct 3D quantitative maps of NFT burden in the MTL at individual and group levels. These maps reveal significant variation in NFT burden along the anterior-posterior axis. While early NFT pathology is thought to be confined to the transentorhinal region, we find similar levels of NFT burden in this region and other MTL subregions, including amygdala, temporopolar cortex, and subiculum/CA1.

neuroscience↗

bioRxiv2020

Gut Microbiota-Targeted Diets Modulate Human Immune Status

Diet modulates the gut microbiome, and gut microbes, in turn, can impact the immune system. Here, we used two gut microbiota-targeted dietary interventions, plant-based fiber or fermented foods, to determine how each influences the human microbiome and immune system in healthy adults. Using a 17-week randomized, prospective study design combined with -omics measurements of microbiome and host, including extensive immune profiling, we found distinct effects of each diet. High-fiber consumers showed increased gut microbiome-encoded glycan-degrading CAZymes despite stable community diversity. Three distinct immunological trajectories in high fiber-consumers corresponded to baseline microbiota diversity. Alternatively, the high-fermented food diet steadily increased microbiota diversity and decreased inflammatory markers. The data highlight how coupling dietary interventions to deep and longitudinal immune and microbiome profiling can provide individualized and population-wide insight. Our results indicate that fermented foods may be valuable in countering the decreased microbiome diversity and increased inflammation pervasive in the industrialized society.

microbiology↗

bioRxiv2020

Transcriptome analysis of EPO and GFP HEK293 Cell-lines Reveal Shifts in Energy and ER Capacity Support Improved Erythropoietin Production in HEK293F Cells

Higher eukaryotic cell lines like HEK293 are the preferred hosts for production of therapeutic proteins requiring human post translational processing. However, recombinant protein production can result in severe stress on the cellular machinery, resulting in limited titre and product quality. To investigate the cellular and metabolic characteristics associated with these limitations, we compared erythropoietin (secretory) and GFP (non-secretory) protein producer HEK293 cell-lines using transcriptomics analysis. Despite the high demand for ATP in all protein producer clones, a significantly higher capacity for ATP production was observed with erythropoietin producers as evidenced by the enrichment of upregulated genes in the oxidative phosphorylation pathway. In addition, ribosomal genes exhibited specific patterns of expression depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased erythropoietin secretion, we detected higher ER stress, including upregulation of the ATF6B gene. Our results are significant in recognizing key pathways for recombinant protein production and identifying potential target genes for further development of secretory power in mammalian cell factories. In BriefAlthough the protein secretion process has been widely studied, the complexity of it leaves many questions with regards to defining bottlenecks for successful protein secretion to be answered. By investigating the transcriptomic profiles of different HEK293 clones with varying translational rates producing either the secreted protein erythropoietin or the intracellular GFP, we reveal that high ATP production and improved capacity of specific post-translational pathways are key factors associated with boosting erythropoietin production. HighlightsO_LITranscriptomics analysis of a panel of HEK293 stable cell lines expressing GFP or erythropoietin (EPO) at varying translational rates C_LIO_LIExpression of mitochondrial ribosomal genes is positively correlated with EPO secretion C_LIO_LIExpression of different cytosolic ribosomal genes are correlated with productivity in a recombinant-protein specific manner C_LIO_LIHigh EPO producing clones have significant upregulation of ATF6B, potentially enabling a beneficial ER stress response to cope with high protein secretion C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/299966v1_ufig1.gif" ALT="Figure 1"> View larger version (87K): org.highwire.dtl.DTLVardef@1e35efforg.highwire.dtl.DTLVardef@71cee2org.highwire.dtl.DTLVardef@1c56d33org.highwire.dtl.DTLVardef@843a6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2020

Machine learning-based investigation of the cancer protein secretory pathway

Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets. Author SummaryThe secretory pathway is a series of intracellular compartments and enzymes that process and export proteins from the cell to the surrounding environment. Dysfunction of the secretory pathway is associated with many diseases, including cancer, and therefore constitutes a potential target for novel therapeutic strategies. The large number of interacting components that comprise the secretory pathway pose a challenge when attempting to identify where the dysfunction originates and/or how to restore healthy function. To improve our understanding of how the secretory pathway is changed within tumors, we used gene expression data from normal tissue and tumor samples from thousands of individuals which included many different types of cancers. The data was analyzed using various machine learning algorithms which we trained to predict sample characteristics, such as disease severity. This training quantified the relative degree to which each gene was associated with the tumor characteristic, allowing us to predict which secretory pathway components were important for processes such as tumor progression--both within specific cancer types and across many different cancer types. Our approach demonstrated excellent performance compared to traditional gene expression analysis methods and identified several secretory pathway components with strong evidence of involvement in tumor development.

systems biology↗

bioRxiv2020

Single cell BCR and transcriptome analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen-specific B cell responses.

B cell responses are a critical component of anti-viral immunity. However, a comprehensive picture of antigen-specific B cell responses, differentiation, clonal proliferation and dynamics in different organs after infection is lacking. Here, we combined single-cell RNA sequencing with single-cell B cell receptor (BCR) characterization of antigen-specific cells in the draining lymph nodes, spleen and lungs after influenza infection. We identify several novel B cell subpopulations forming after infection and find organ-specific differences that persist over the course of the response. We discover important transcriptional differences between memory cells in lungs and lymphoid organs and describe organ-restricted clonal expansion. Strikingly, by combining BCR mutational analysis, monoclonal antibody expression and affinity measurements we find no differences between germinal center (GC)-derived memory and plasmacells, at odds with an affinity-based selection model. By linking antigen-recognition with transcriptional programming, clonal-proliferation and differentiation, these finding provide important advances in our understanding of antiviral B cell immunity.

immunology↗

bioRxiv2020

Transcriptome Sequencing Reveals Sex Differences in Human Meniscal Cell Response to Estrogen Based on Dosing Kinetics

Osteoarthritis is a disease marked by progressive and irreversible hyaline cartilage and fibrocartilage breakdown that affects the lives of millions of patients worldwide. Female sex and menopause are both risk factors for knee osteoarthritis, indicating that estrogen could play a role in this disease. In this study, RNA sequencing was used to determine the effects of estrogen treatment on human meniscal cells. Differences in the number and type of differentially expressed genes were seen based on donor sex, estrogen dose, and dosing kinetics. Significantly more differentially expressed genes were seen from male meniscal cells in response to all dosing conditions compared to female cells. Importantly, more genes were differentially expressed in cells treated with continuous dosing of estrogen, which has been shown to stimulate genomic estrogen signaling, as compared to pulsed dosing. Additionally, functional enrichment analysis revealed that many genes of the extracellular matrix, which is important for joint health and injury repair, were differentially expressed. Overall, this initial study lays the groundwork for future avenues to pursue the effect of estrogen delivery on regenerative pathways. This critical analysis will then inform the design and implementation of estrogen replacement therapies to promote meniscal health and reduce the onset of osteoarthritis.

genomics↗

bioRxiv2019

Long-term dietary intervention reveals resilience of the gut microbiota despite changes in diet and weight

With the rising rates of obesity and associated metabolic disorders, there is a growing need for effective long-term weight loss strategies, coupled with an understanding of how they interface with host physiology. While diet is a critical and promising area of focus, it has been difficult to identify diets that are broadly effective in long-term weight management. To explore the interaction between specific diets and bacteria within the gut, we tracked microbiota composition over a 12-month period as part of a larger dietary intervention study of participants consuming either a low-carbohydrate or low-fat diet. While baseline microbiota composition was not predictive of weight loss, each diet resulted in substantial changes in the microbiota three months after the start of the intervention; some of these changes were diet-specific and others tracked with weight loss. After these initial shifts, the microbiota returned near its original baseline state for the remainder of the intervention, despite participants maintaining their diet and weight loss for the entire study. These results suggest a resilience to perturbation of the microbiomes starting state. When considering the established contribution of obese-associated microbiotas to weight gain in animal models, microbiota resilience may need to be overcome for long-term alterations to host physiology.

microbiology↗

bioRxiv2019

Defining and predicting transdiagnostic categories of neurodegenerative disease

In current models of neurodegeneration, individual diseases are defined by the presence of one or two pathogenic protein species. Yet, it is the rule rather than the exception that a patient meets criteria for more than one disease. This fact often remains hidden until autopsy, when neuropathological evaluation can assign disease labels based on gold-standard criteria. Ultimately, the prevalence of concomitant diagnoses and the inability to infer an underlying neuropathological syndrome from clinical variables hinders the identification of patients who might be good candidates for a particular intervention. Here, by applying graph-based clustering to post-mortem histopathological data from 1389 patients with degeneration in the central nervous system, we generate 4 non-overlapping, data-driven disease categories that simultaneously account for amyloid-{beta} plaques, tau neurofibrillary tangles, -synuclein inclusions, neuritic plaques, TDP-43 inclusions, angiopathy, neuron loss, and gliosis. The resulting disease clusters are transdiagnostic in the sense that each cluster contains patients belonging to multiple different existing disease diagnoses, who colocalize in clusters according to the pathogenic protein aggregates known to drive each disease. We show that our disease clusters, defined solely by histopathology, separate patients in terms of cognitive phenotypes, cerebrospinal fluid (CSF) protein levels, and genotype in a manner that is not trivially explained by the representation of individual diseases within each cluster. Finally, we use cross-validated multiple logistic regression to generate high accuracy predictions (AUC > 0.9) of membership to both existing disease categories and transdiagnostic clusters based on CSF protein levels and genotype, both accessible in vivo. Broadly, our approach parses phenotypic and genotypic heterogeneity in neurodegenerative disease, and represents a general framework for identifying otherwise-fuzzy disease subtypes in other areas of medicine, such as epilepsy, vascular disease, and cancer. In clinical neurology, the statistical models we generate may be useful for repurposing drugs by comparing efficacy to probabilistic estimates of disease cluster membership, as well as for future trials that could be targeted towards an algorithmically defined family of diseases.

neuroscience↗