Extended Data Fig. 1: Filtering of LiRA-called sSNVs to minimize single-cell artefacts from MDA amplification.

a, Total pre-filtering LiRA-called sSNV per genome for control and AD single neurons. Single neuronal nuclei from prefrontal cortex (PFC) and hippocampal CA1 (HC) underwent scWGS (45X targeted average coverage). Genome-wide counts of sSNV were determined using linked-read analysis (LiRA). Per genome sSNV counts for all control and AD neurons are shown here, prior to signature-based filtering. b, Total pre-filtering LiRA-called sSNV per genome plotted against raw LiRA-called sSNVs, an intermediate metric in the LiRA calling pipeline prior to power ratio adjustment for genome coverage and false positive rate. c, Single neuron sSNV counts in relation to coverage evenness of genome sequencing. Total pre-filtering LiRA-called sSNV counts from single neuronal nuclei are shown in relation to median absolute pairwise difference (MAPD) scores for the coverage evenness of each cell. At very high MAPD scores (>2.0), sSNV counts increase with MAPD, raising concern for artefactual sSNV calls in these cells owing to uneven genome coverage. d, e, Using NMF mutational signature analysis, the sSNV contribution was determined for two signatures potentially representing single-cell amplification artefacts: SBS scE and SBS scF²⁴. For signature, the mutation type frequency for each trinucleotide context is shown above the sSNV plot. SBS scF is composed of C>T changes, while SBS scE is characterized by a particular subset of C>T, GC>GT. Signature SBS scE showed elevation in cells with MAPD >2.0. Signature SBS scF shows a relationship between uneven amplification (high MAPD) and SBS scF, perhaps owing to allele dropout causing single strand lesions to be read as somatic mutations. A subset of AD neurons showed LiRA-called pre-filtering sSNV counts >20,000/neuron and substantial component of potential artefact signature SBS scE. These neurons may represent an agonal ‘ultramutated’ state, but were not included in subsequent analyses owing to the abundance of potential artefact signature SBS scE (see g). f, Schematic for potential generation of artefactual sSNV in scWGS owing to uneven coverage. The scWGS LiRA platform calls sSNVs that are linked by sequencing reads to heterozygous germline single nucleotide polymorphisms (SNPs) (left). A single-stranded lesion of DNA damage, such as oxidation or alkylation, is paired with an unmodified base on the opposite genomic strand, such that LiRA would not call a sSNV under conditions of sufficiently even sequencing coverage (middle). However, if severe non-uniformity in strand-specific amplification (strand dropout) occurred, the single-stranded DNA lesion (or a polymerase error on one strand) could be erroneously called as an sSNV (right). For this reason, severely uneven single-cell genome amplification could produce artefactual LiRA sSNV calls. g, Analysis pipeline for minimization of potential artefacts of single-cell genome amplification and sequencing. Using our observations and advances reported in Petljak et al.²⁴, we developed a computational pipeline to generate a set of higher-confidence filtered sSNV calls. This pipeline uses SNP-phased SNVs called by linked-read analysis (LiRA), and applies 3 additional specific steps to the initial variant call set: 1) Removal of single neurons which display widely uneven genome amplification, as indicated by MAPD score >2.0, above which the number of sSNVs increases (see c), raising concern for false positive variant calls due to uneven genome coverage; 2) Removal of single neurons whose mutational profile is dominated by the potential artefact mutational signature SBS scE (see d); and 3) Removal from each neuron the contribution of variants from the potentially artefactual signatures SBS scE and SBS scF. These steps produce counts of higher-confidence filtered sSNVs from single neurons. Although mutational signatures SBS scE and SBS scF have been previously reported as a potential artefact of single-cell genome amplification, the signal does potentially carry biological information. However, in this study we exclude these variants so as to minimize the influence of potential artefactual sSNV calls, to focus our analysis on the higher-confidence filtered sSNVs.

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