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Lodato, M.

Publications and source records attributed to Lodato, M..

2 recordsLinked to original sources

Cell lineage analysis with somatic mutations reveals late divergence of neuronal cell types and cortical areas in human cerebral cortex

The mammalian cerebral cortex shows functional specialization into regions with distinct neuronal compositions, most strikingly in the human brain, but little is known in about how cellular lineages shape cortical regional variation and neuronal cell types during development. Here, we use somatic single nucleotide variants (sSNVs) to map lineages of neuronal sub-types and cortical regions. Early-occurring sSNVs rarely respect Brodmann area (BA) borders, while late-occurring sSNVs mark neuron-generating clones with modest regional restriction, though descendants often dispersed into neighboring BAs. Nevertheless, in visual cortex, BA17 contains 30-70% more sSNVs compared to the neighboring BA18, with clones across the BA17/18 border distributed asymmetrically and thus displaying different cortex-wide dispersion patterns. Moreover, we find that excitatory neuron-generating clones with modest regional restriction consistently share low-mosaic sSNVs with some inhibitory neurons, suggesting significant co-generation of excitatory and some inhibitory neurons in the dorsal cortex. Our analysis reveals human-specific cortical cell lineage patterns, with both regional inhomogeneities in progenitor proliferation and late divergence of excitatory/inhibitory lineages.

neuroscience↗

PaSD-qc: Quality control for single cell whole-genome sequencing data using power spectral density estimation

Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, scWGS introduces DNA amplification-related biases that can confound downstream analysis. Here we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance, and inter-sample consistency as well as identifies aberrantly amplified chromosomes. We demonstrate the usefulness of this tool in evaluating scWGS protocols and in selecting high-quality libraries from low-coverage data for deep sequencing.

bioinformatics↗