Biology subjects

Candia, J.

Publications and source records attributed to Candia, J..

14 recordsLinked to original sources

bioRxiv2025

The blood proteome predicts the impact of circulating factors on age-related mitochondrial health

Circulating non-cellular factors, such as plasma proteins, contribute to various features of aging. To determine the impacts of endogenous circulating factors on human age-related bioenergetic decline, we treated primary human fibroblasts with serum samples representing the adult life-course. Our results demonstrate that the maximal mitochondrial bioenergetic capacity of fibroblasts treated with serum is negatively correlated with the chronological and epigenetic age of the serum donor. Using targeted proteomics, we identified plasma proteins associated with the bioenergetic effects of serum. We then utilized elastic net, a linear regression modeling technique, to derive a novel proteomic signature of age-related mitochondrial differences. MitoAge is a 25-protein signature of age-related mitochondrial health that predicts the systemic bioenergetic effects of circulating factors and is related to differences in physical function across human aging. Signatures that report on cellular hallmarks of aging, such as mitochondrial function, represent a new generation of mechanistically-informed biomarkers of biological aging. GRAPHICAL ABSTRACTIn this study, we describe the development of a novel proteomic signature, MitoAge. This signature was developed by utilizing human primary fibroblasts treated with serum samples representing the adult human life-course and analyzing how resulting respirometry correlated with chronological age, epigenetic age, and abundance of serum proteins. Utilizing machine learning techniques, we derived a 25-protein signature which can predict bioenergetics and physical features related to aging. Application and utility of this signature may be used to identify novel drivers of health and longevity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/687483v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@905c12org.highwire.dtl.DTLVardef@18ca82forg.highwire.dtl.DTLVardef@175d397org.highwire.dtl.DTLVardef@966faa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

SomaModules: a pathway enrichment approach tailored to SomaScan data

Motivated by the lack of adequate tools to perform pathway enrichment analysis, this work presents an approach specifically tailored to SomaScan data. Starting from annotated gene sets, we developed a greedy, top-down procedure to iteratively identify strongly intra-correlated SOMAmer modules, termed "SomaModules", based on 11K SomaScan data. We generated two repositories based on the latest MSigDB and MitoCarta releases, containing more than 40,000 SOMAmer-based gene sets combined. These repositories can be utilized by any unstructured pathway enrichment analysis tool. We validated our results with two case examples: (i) Alzheimers Disease specific pathways in a 7K SomaScan case-control study, and (ii) mitochondrial pathways using 11K SomaScan data linked to physical performance outcomes. Using Gene Set Enrichment Analysis (GSEA), we found that, in both examples, SomaModules had significantly higher enrichment than the original gene set counterparts. These findings were robust and not significantly affected by the choice of enrichment metric or the Kolmogorov enrichment statistic used in the GSEA procedure. We provide users with access to all code, documentation and data needed to reproduce our current repositories, which also will enable them to leverage our framework to analyze SomaModules derived from other sources, including custom, user-generated gene sets.

bioinformatics↗

bioRxiv2025

Changes in motor unit conduction velocity after unilateral lower limb suspension and active recovery correlate with muscle ion channel gene expression

The effects of muscle disuse on the propagation of action potentials along muscle units, a key process for effective muscle activation and force production, remain poorly understood. This study aimed to investigate changes in action potential propagation and to identify biological factors influencing these changes following unilateral lower limb suspension (ULLS) and active recovery (AR). Eleven young male participants underwent 10 days of ULLS followed by 21 days of AR based on resistance exercise. Maximal force of the knee extensor muscles (MVC), High-Density surface EMG recordings and muscle biopsies of the vastus lateralis muscle were collected before ULLS, after ULLS and after AR. EMG recordings collected during submaximal isometric contractions were decomposed to estimate single motor unit conduction velocity (MU CV). Muscle biopsies were used to measure muscle fibre diameters via histochemical analysis and ion channel transcriptomic profiles via mRNA-sequencing. MVC decreased after ULLS by 29% and fully recovered after AR. MU CV decreased after ULLS and fully recovered, up to exceeding baseline values after AR. Muscle fibre diameters did not change across the interventions and showed no correlation with MU CV. Conversely, a feature importance analysis revealed that mRNA expression levels of specific ion channel genes, particularly those involved in K+ transport, correlate with MU CV at baseline and across the interventions. This study highlights the crucial role of K+ ion channels in influencing MU CV in humans, offering new insights into MU CV modulation and the mechanisms of muscle force changes after disuse and active recovery. Key pointsO_LIMuscle disuse, such as in unilateral lower limb suspension, leads to a decrease in motor unit conduction velocity (MU CV), a critical factor for muscle activation and force production. C_LIO_LIActive recovery through resistance exercise results in the full recovery of MU CV, even exceeding baseline levels. C_LIO_LIMuscle fibre diameters do not change significantly after limb suspension or active recovery and show no correlation with MU CV. C_LIO_LIConversely, ion channel mRNA expression, particularly of those related to K+ transport, correlates with MU CV and its changes following disuse and recovery. C_LIO_LIThese findings highlight K+ ion channels as a key factor in regulating MU CV in humans and provide new molecular determinants of the changes in muscle force after disuse and recovery. C_LI

physiology↗

bioRxiv2024

Effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification using the SomaScan and Olink assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supplementary Data files to be used as resources for the growing user communities of both platforms.

molecular biology↗

bioRxiv2024

Variability of 7K and 11K SomaScan plasma proteomics assays

SomaScan is an aptamer-based proteomics assay designed for the simultaneous measurement of thousands of human proteins with a broad range of endogenous concentrations. The 7K SomaScan assay has been recently expanded into the new 11K version. Following up on our previous assessment of the 7K assay, here we expand our work on technical replicates from donors enrolled in the Baltimore Longitudinal Study of Aging. By generating SomaScan data from a second batch of technical replicates in the 7K version, as well as additional intra- and inter-plate replicate measurements in the new 11K version using the same donor samples, this work provides useful precision benchmarks for the SomaScan user community. Beyond updating our previous technical assessment of the 7K assay with increased statistics, here we estimate inter-batch variability, we assess inter- and intra-plate variability in the new 11K assay, we compare the observed variability between the 7K and 11K assays (leveraging the use of overlapping pairs of technical replicates), and explore the potential effects of sample storage time (ranging from 2 to 30 years) in the assays precision.

systems biology↗

bioRxiv2024

SomaScan Bioinformatics: Normalization, Quality Control, and Assessment of Pre-Analytical Variation

SomaScan is an aptamer-based proteomics assay designed for the simultaneous measurement of thousands of human proteins with a broad range of endogenous concentrations. In its most current version released on November 1, 2023, the 11K SomaScan assay v5.0 is capable of measuring 10,776 human proteins covering major biological processes and disease areas, including cardiology, inflammation, neurology, and oncology. Here, I review bioinformatic approaches to perform normalization, quality control, and variability assessments.

bioinformatics↗

bioRxiv2024

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

bioinformatics↗

bioRxiv2023

Epigenetic signature of human immune aging: the GESTALT study.

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood borne immune cell types (naive B, naive CD4+and CD8+ T cells, granulocytes, monocytes and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousand of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In-silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1{beta}) and REST transcription factor motifs respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to transcription factor motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

molecular biology↗

bioRxiv2022

Assessment of Variability in the Plasma 7k SomaScan Proteomics Assay

SomaScan is a high-throughput, aptamer-based proteomics assay designed for the simultaneous measurement of thousands of proteins with a broad range of endogenous concentrations. In its most current version, the 7k SomaScan assay v4.1 is capable of measuring 7,288 human proteins. In this work, we present an extensive technical assessment of this platform based on a study of 2,050 samples across 22 plates. Included in the study design were inter-plate technical duplicates from 102 human subjects, which allowed us to characterize different normalization procedures, evaluate assay variability by multiple analytical approaches, present signal-over-background metrics, and discuss potential specificity issues. By providing detailed performance assessments on this wide range of technical aspects, we aim for this work to serve as a valuable resource for the growing community of SomaScan users.

molecular biology↗

bioRxiv2020

mutSigMapper: an R package to map spectra to mutational signatures based on shot-noise modeling

SummarymutSigMapper aims to resolve a critical shortcoming of existing software for mutational signature analysis, namely that of finding parsimonious and biologically plausible exposures. By implementing a shot-noise-based model to generate spectral ensembles, this package addresses this gap and provides a quantitative, non-parametric assessment of statistical significance for the association between mutational signatures and observed spectra. Availability and implementationThe mutSigMapper R package is available under GPLv3 license at https://github.com/juliancandia/mutSigMapper. Its documentation provides additional details and demonstrates applications to biological datasets.

bioinformatics↗

bioRxiv2020

Single-cell atlas of tumor clonal evolution in liver cancer

Tumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure and patients prognosis. Here we performed single-cell transcriptome profiling of 46 primary liver cancers from 37 patients enrolled for interventional studies. We surveyed the landscape of ~57,000 malignant and non-malignant cells and determined tumor cell clonality by developing a machine learning-based consensus clustering method. We found evidence of tumor cell branching evolution using hierarchical clustering, RNA velocity as well as reverse graph embedding methods. Interestingly, an increasing tumor cell clonality was tightly linked to patients prognosis, accompanied by a polarized immune cell landscape. We identified osteopontin as a key player for tumor cell evolution and microenvironmental reprogramming. Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers for tumor evolution in response to therapy.

cancer biology↗

bioRxiv2019

A Modified Injector and Sample Acquisition Protocol Can Improve Data Quality and Reduce Inter-Instrument Variability of the Helios Mass Cytometer

Mass cytometry is a powerful tool for high dimensional single cell characterization. Since the introduction of the first commercial CyTOF mass cytometer by DVS Sciences in 2009, mass cytometry technology has matured and become more widely utilized, with sequential platform upgrades designed to address specific limitations and to expand the capabilities of the platform. Fluidigms 3rd-generation Helios mass cytometer introduced a number of upgrades over the previous CyTOF2. One of these new features is a modified narrow bore sample injector that generates smaller ion clouds, which is expected to improve sensitivity and throughput. However, following rigorous testing we find that the narrow-bore sample injector may have unintended negative consequences on data quality and result in lower median signal intensities and higher coefficients of variation in antibody expression. We describe an alternative Helios acquisition protocol using a wider bore injector, which largely mitigates these data quality issues. We directly compare these two protocols in a multi-site study of 10 Helios instruments across 7 institutions and show that the modified protocol improves data quality and reduces inter-instrument variability. These findings highlight and address an important source of technical variability in mass cytometry experiments that is of particular relevance in the setting of multi-center studies.

immunology↗

bioRxiv2018

RefCell: Multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’

BackgroundImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Cutting-edge high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but they suffer from the \"curse of dimensionality\" and non-standardized outputs.\n\nResultsHere we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states, and uses these \"typical cells\" as a reference for classification and weighting of metrics. RefCell quantitatively assesses the heterogeneous deviations from typical behavior for each analyzed perturbation or sample.\n\nConclusionsWe apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin protein targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering based single cell analysis method, which both reveal more potential hits than conventional average based analysis.

bioinformatics↗

bioRxiv2018

eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

SummarySystems biology analysis often involves building predictive models by selecting informative features from a large number of measurements. The elastic net for generalized linear models is a popular regression and feature selection method, particularly useful when the number of features is greater than the sample size or when there exist many correlated predictor variables. This package provides a quantitative, cross-validation based toolkit to evaluate elastic net models and to uncover correlates contributing to prediction. Feature importance is evaluated by flexible criteria using out-of-bag prediction performance assessed via user-defined quality functions. Statistical significance is assigned to each model by comparison to null models generated by permutations of sample labels; analogous approaches are used to assess significance for the contribution of individual features to prediction. This package fits linear, binomial (logistic) and multinomial models, and provides a set of standard plots, summary statistics and output tables. eNetXplorer enables quantitative, exploratory analysis to generate hypotheses on which features may be associated with biological phenotypes of interest, such as in the identification of biomarkers for therapeutic responsiveness.\n\nAvailability and implementationThe eNetXplorer R package is available under GPLv3 license at https://CRAN.R-project.org/package=eNetXplorer

systems biology↗