Biology subjects

Gomez-Cano, F.

Publications and source records attributed to Gomez-Cano, F..

5 recordsLinked to original sources

bioRxiv2026

Empirical Estimation of Ambient Contamination in Combinatorial Single-Cell Methods Using Multi-Reference Mapping

Droplet-based microfluidics and combinatorial indexing (scifi-ATAC and scifi-RNA) have made single-cell experiments massively scalable. However, higher-order multiplexing complicates data quality, introduces noise, and affects the potential for biological discovery. Here, we show that ambient chromatin accumulates through the experimental workflow and distorts chromatin profiles, most drastically in low-depth nuclei and in minority populations. Standard cell calling relies heavily on read count thresholds, while existing decontamination methods generally operate on aggregated count matrices rather than the underlying reads. We introduce scifi-demux, for preprocessing scifi-ATAC libraries, and AmbientMapper, a generative model that maps reads competitively against multiple references, learns the ambient profile from empty and low-complexity barcodes, and separates nuclei from background and singlets from doublets by Bayesian Information Criterion. Using interspecies ground truth experiments, published multi-genotype libraries, and simulated read-level synthetic barcodes in which every contaminating read is traceable, we show that calls are robust to parameter variation and stable across designs, achieving a wrong-genome rate of 0.19% on a 26-genome reference panel. Finally, we evaluated the impact of removing contaminants, showcasing how AmbientMapper rescues low-depth nuclei discarded by standard pipelines and restores biological structure obscured by contamination.

bioinformatics↗

bioRxiv2026

Cell-type-specific regulatory variation shapes maize heterosis

Heterosis, the superior performance of hybrids over their parents, has been widely exploited to enhance crop productivity, but its underlying regulatory mechanisms remain incompletely understood. In particular, how cis-regulatory elements (CREs) vary between parents and hybrids, and how this variation is organized across cell types, remains largely unresolved. Here we profiled chromatin accessibility in 131,890 nuclei derived from seedlings of three Zea mays (maize) inbred lines and their reciprocal hybrids, resolving 14 major cell types. Parental haplotype comparisons showed that most accessible chromatin regions (ACRs) were sequence-conserved and have similar binarized chromatin accessibility status. At cellular resolution, hybridization broadly attenuated chromatin accessibility cell-type specificity, weakening parental cell-type bias and yielding a more even chromatin accessibility profile across cellular contexts. Chromatin inheritance was strongly cell-type dependent, with non-additive inheritance preferentially concentrated at cell-type-specific ACRs. Although cis effects were most prevalent overall, ACRs with attenuated cell-type specificity in hybrids were enriched for non-additive inheritance and trans effects. Attenuated ACRs were more prone to transcription factor (TF) footprint gains than loci retaining high cell-type specificity, with DNA-BINDING WITH ONE FINGER (DOF) and VASCULAR PLANT ONE ZINC FINGER (VOZ) among the motif families most enriched in high-confidence footprint-gaining events. ACRs with footprint gains were preferentially linked to genes involved in development, hormone responses and growth, including DOF-family gains at GOLDEN2-like (GLK2), where both the motif family and the locus have been implicated in bundle-sheath development and C4 photosynthetic specialization. Together, our findings reveal that parental chromatin accessibility patterns are reconfigured across cell types in maize hybrids, with attenuation of cell-type specificity offering a potential cellular mechanism underlying heterosis.

plant biology↗

bioRxiv2026

Cell-specific dedifferentiation in Arabidopsis thaliana

Single somatic plant cells are capable of dedifferentiation and reversion to totipotency. However, understanding of dedifferentiation remains incomplete, limiting efforts to reprogram plant cell identities with engineered properties. Here, we describe the transcriptional dynamics of 125,091 Arabidopsis thaliana whole seedling protoplasts along a time course of dedifferentiation and varied chemical perturbations using single-cell RNA sequencing. We observed widespread cell-type-specific transcriptional reprogramming concomitant with cell wall digestion, including a guard cell-specific somatic embryogenic-like program initiated by ectopic LEC2 expression. Contrasting cell states between chemical perturbations across real time revealed that phytohormones antagonize cell aging, promote retention of transcriptional diversity, and selectively suppress wound responses. By dissecting time-resolved transcriptional dynamics, we discovered that dedifferentiation is accompanied by the activation of stem-cell and meristematic marker genes and associated with prevalent chromatin remodeling. Despite discovery of a shared regulatory program underlying early dedifferentiation, temporal modeling of diverse somatic cell types uncovered extensive bias in dedifferentiation potential, including epithem-derived dedifferentiated cells, variable dedifferentiation rates, and diversity in early reprogramming outcomes as a major source of in vitro heterogeneity. Taken together, these data showcase how initial somatic cell identity, phytohormone signaling, chromatin remodeling, and reprogramming heterogeneity combine to coordinate the early transcriptional events defining cellular dedifferentiation.

plant biology↗

bioRxiv2025

Polymer-derived distance penalties improve chromatin interaction predictions from single-cell data across crop genomes

Scalable proxies for 3D genome contacts - such as single-cell co-accessibility and deep learning predictions - have emerged as powerful alternatives to chromatin capture-based methods, but predictions systematically overestimate long-range interactions. Here we show how to correct this bias using distance-based penalty functions informed by Gaussian mixture modeling and polymer-physics scaling. Using Hi-C datasets from maize, rice, and soybean, we derive tissue-specific and global consensus penalties parameterized by multi-regime power-law exponents. Applying these corrections to scATAC-seq co-accessibility scores improves their distance profiles in concordance with Hi-C and reduces long-range false positives by an average of 73% with tissue-specific penalties and 66% with the global consensus. We provide open-source code and fitted parameters to support adoption in maize, rice, and soybean.

bioinformatics↗

bioRxiv2020

Co-expression signatures of combinatorial gene regulation

Gene co-expression analyses provide a powerful tool to determine gene associations. The interaction of transcription factors (TFs) with their target genes is an essential step in gene regulation, yet to what extent TFs-target gene associations are recovered in co-expression studies remains unclear. Using the wealth of data available for Arabidopsis, we show here that protein-DNA interactions are overall poor indicators of TF-target co-expression, yet the inclusion of TF-TF interaction information significantly enhance co-expression signals. These results highlight the impact of combinatorial gene control on such gene association networks. We integrated this information to predict higher-order regulatory complexes, which are difficult to identify experimentally. We demonstrate that genes strongly co-expressed with a TF are also enriched in indirect targets. Our results have significant implications on the empirical understanding of complex gene regulatory networks and transcription factor function, and the significance of co-expression from the perspective of protein-protein and protein-DNA interactions.

systems biology↗