Biology subjects

Lopez, C. M.

Publications and source records attributed to Lopez, C. M..

4 recordsLinked to original sources

bioRxiv2026

A generalizable normalization framework to decouple protocol and instrument effects: Application to high-sensitivity proteomics multicentric study (PME13)

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

bioinformatics↗

bioRxiv2026

Neuropixels 1536 Channel Quad Base probe reveals brain-wide communication underlying flexible sensorimotor sequences

The brain orchestrates billions of neurons spanning hundreds of regions to drive behavior. High-density electrophysiology has provided insights into this process, but monitoring brain-wide patterns of spiking activity demands new tools. Here we present Neuropixels 2.0 Quad Base, a 1536-channel probe with four times the simultaneous recording capacity of state-of-art Neuropixels 2.0. Using two Quad Base probes, we performed 3072-channel recordings in mice trained to produce complex sequences of goal-directed licks. We decoded behavioral kinematics from neural populations across more than 20 brain regions and identified the spatiotemporal emergence of motor decision signals. The probes high channel count further revealed expanded inter-neuronal functional subnetworks and latent dynamics across multiple areas--features significantly undersampled when channels were subsampled to mimic standard Neuropixels 2.0. Neuropixels 2.0 Quad Base offers a powerful tool to explore neural encoding of cognition and behavior at grand scale.

neuroscience↗

bioRxiv2020

Elucidating dynamics and regulation of alternative splicing in osteogenic differentiation

Nearly all human multi-exonic genes undergo alternative splicing (AS) via regulation by RNA-binding proteins (RBPs), but few studies have examined the temporal dynamics of AS and its regulation during cell differentiation in the bone niche. We sought to evaluate how AS, under the control of RBPs, affects cell fate commitment during induced osteogenic differentiation of human bone marrow-derived multipotent stem/stromal progenitor cells (MSPCs). We generated a time-course RNA sequencing (RNA-seq) dataset representative of induced MSPC differentiation to osteoblasts. Our analysis revealed widespread AS changes, coordinated with differential RBP expression, at multiple time points, including many AS changes in non-differentially expressed genes. We also developed a computational approach to profile the dynamics and regulation of AS by RBPs using time-course RNA-seq data, by combining temporal patterns of exon skipping and RBP expression with RBP binding sites in the vicinity of regulated exons. In total we identified nine RBPs as potential key splicing regulators during MSPC osteogenic differentiation. Perturbation of one candidate, KHDRBS3, inhibited osteogenesis and bone formation in vitro, validating our computational prediction of "driver" RBPs. Overall, our work highlights a high degree of complexity in the splicing regulation of MSPC osteogenic differentiation. Our computational approach may be applied to other time-course data to explore dynamic AS changes and associated regulatory mechanisms in other biological processes or disease trajectories.

bioinformatics↗

bioRxiv2019

Multi-Dimensional Machine Learning Approaches for Fruit Shape Recognition and Phenotyping in Strawberry

BackgroundShape is a critical element of the visual appeal of strawberry fruit and determined by both genetic and non-genetic factors. Current fruit phenotyping approaches for external characteristics in strawberry rely on the human eye to make categorical assessments. However, fruit shape is multi-dimensional, continuously variable, and not adequately described by a single quantitative variable. Morphometric approaches enable the study of complex forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the principal progression of k clusters (PPKC). We use these human-recognizable shape categories to select features extracted from multiple morphometric analyses that are best fit for genome-wide and forward genetic analyses.\n\nResultsWe transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered four principal shape categories, and inferred progression using PPKC. We extracted 67 quantitative features from digital images of strawberries using a suite of morphometric analyses and multi-variate approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68.9 - 99.3% for the newly created, genetically correlated phenotypic variables describing a shape.\n\nConclusionsOur results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches we applied in strawberry should apply to other fruits, vegetables, and specialty crops.

plant biology↗