Biology subjects

Paldi, A.

Publications and source records attributed to Paldi, A..

8 recordsLinked to original sources

bioRxiv2025

Genomic Informational Field Theory (GIFT) to map complex traits in small sample sizes

Genome-wide association studies (GWAS) are commonly used to investigate the genetic basis of complex traits. However, to be adequately powered they typically require large sample sizes to provide precise inferences. To address this challenge, this paper introduces GIFT, a novel data analytic method that enhances the power of genetic analyses, enabling the use of smaller datasets without compromising precision. In a small cohort of 157 ponies, GIFT was applied to examine the complex trait of "height at withers", comparing its performance to traditional GWAS. GIFT enabled the identification genetic loci linked to insulin physiology validating, in turn, a long-standing hypothesis that "height at withers" is associated with insulin physiology in equids, potentially promoting equine metabolic syndrome (EMS). By redefining correlations between single nucleotide polymorphisms (SNPs), GIFT provides new insights into linkage disequilibrium and reveals underlying gene network structures. This, in turn, enables the distinction between core and peripheral genes within these networks. By reducing the time and cost associated with large-scale genotype-phenotype mapping studies without sacrificing statistical robustness, GIFT broadens access to quantitative genetic research, allowing smaller-scale studies to investigate the genetic architecture of complex traits with greater resolution. NEW & NOTEWORTHYInferring genotype-phenotype associations typically require large sample sizes, limiting many genetic studies. GIFT, a novel data analytics tool, overcomes this by enabling accurate association mapping in small datasets. We further show how GIFTs extended framework infers linkage disequilibrium and gene networks, distinguishing core from peripheral genes involved in complex traits. This advancement enhances understanding of biological architecture and enables high-resolution genetic research in limited cohorts, offering a powerful, cost-effective alternative to traditional large-scale approaches.

genetics↗

bioRxiv2024

Investigative power of Genomic Informational Field Theory (GIFT) relative to GWAS for genotype-phenotype mapping

Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R.A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic Informational Field Theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Whilst GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in ovis aries related to bone growth (Dataset-1) and to a series of linked metabolic and epigenetic pathways (Dataset-2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked. NEW & NOTEWORTHYThe genetic basis of complex traits remains challenging to investigate using classic GWASs. Given the success of gene editing technologies this point needs to be addressed urgently since there can only be useful editing technologies if precise genotype-phenotype mapping information is available initially. GIFT is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.

genetics↗

bioRxiv2023

Metabolic adaptation pilots the differentiation of human hematopoietic cells

A continuous supply of energy and metabolic intermediates is an essential prerequisite for survival and the highest priority for the cell. We hypothesize that cell differentiation is a process of optimization of energy flow in a changing environment through phenotypic adaptation. A mechanistic basis of this hypothesis is provided by the established link between core energy metabolism and epigenetic covalent modifications of chromatin. This hypothesis predicts that early metabolic perturbations impact subsequent differentiation. To test this, we induced transient metabolic perturbations in undifferentiated human hematopoietic cells using pharmacological inhibitors targeting key metabolic reactions. We recorded changes in chromatin structure and gene expression, as well as phenotypic alterations by single-cell ATAC and RNA sequencing, time-lapse microscopy and flow cytometry. Our observations suggest that these metabolic perturbations are shortly followed by alterations in chromatin structure, leading to changes in gene expression. We also show that these transient fluctuations alter the differentiation potential of the cells.

cell biology↗

bioRxiv2023

An Image-Guided Microfluidic System for Single-Cell Lineage Tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which is compatible with downstream single-cell RNA sequencing analysis.

cell biology↗

bioRxiv2022

Differentiation is accompanied by a progressive loss in transcriptional memory

Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (sc-RT-qPCR and scRNA-seq). We identified that the gene transcription profiles of differentiating sister-cells are more similar to each-other than to those of non related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister-cells than between self-renewing sister-cells. Furthermore, a continuous increase in this divergence from first generation to second generation was observed when comparing differentiating cousin-cells to self renewing cousin-cells. Our results are in favor of a continuous and gradual erasure of transcriptional memory during the differentiation process.

cell biology↗

bioRxiv2020

Selective silencing rather than targeted activation of gene expression underlies fate choice in human hematopoietic stem cells.

When human cord blood derived CD34+ cells are induced to differentiate in vitro, they undergo rapid and dynamic morphological and molecular transformations that are critical for fate commitment. Using ATAC-seq and single-cell RNA sequencing, we detected two phases in this process. In the first phase, we observed a rapid and global chromatin opening that makes most of the gene promoters in the genome accessible, followed by widespread upregulation of gene transcription and a concomitant increase in the cell-to-cell variability of gene expression. The second phase is marked by a slow chromatin closure and a subsequent overall downregulation of gene transcription and emergence of coherent expression profiles corresponding to distinct cell subpopulations. These observations are consistent with a model based on the spontaneous probabilistic organization of the cellular process of fate commitment.

cell biology↗

bioRxiv2020

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

We employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddingtons epigenetic landscape, that is generalizable to every cell differentiation system.

bioinformatics↗

bioRxiv2017

Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment.

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

cell biology↗