Content uploaded by Ester Kalef-Ezra
Author content
All content in this area was uploaded by Ester Kalef-Ezra on Aug 10, 2023
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
1
Single-cell somatic copy number variants in brain using different amplification methods
and reference genomes
Ester Kalef-Ezra1,2, Zeliha Gozde Turan1,2, Diego Perez-Rodriguez1, Ida Bomann1, Sairam
Behera3, Caoimhe Morley1, Sonja W. Scholz4,5, Zane Jaunmuktane1,2,6, Jonas
Demeulemeester2,7-9, Fritz J Sedlazeck2,3,10,11, Christos Proukakis1,2*
1 Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of
Neurology, London, UK.
2 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase,
MD, 20815.
3 Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston
TX 77030, USA.
4 Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and
Stroke, Bethesda, MD, USA.
5 Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA.
6 Queen Square Brain Bank for Neurological disorders, UCL Queen Square Institute of
Neurology, London, UK.
7 Department of Oncology, KU Leuven, Leuven, Belgium.
8 Cancer Genomics Laboratory, The Francis Crick Institute, London, UK.
9 VIB Center for Cancer Biology, Leuven, Belgium.
10 Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA.
11 Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, USA.
* email: c.proukakis@ucl.ac.uk
Abstract
The presence of somatic mutations, including copy number variants (CNVs), in the brain is well
recognized. Comprehensive study requires single-cell whole genome amplification, with several
methods available, prior to sequencing. We compared PicoPLEX with two recent adaptations of
multiple displacement amplification (MDA): primary template-directed amplification (PTA) and
droplet MDA, across 93 human brain cortical nuclei. We demonstrated different properties for
each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct
chimeric profiles. Furthermore, we performed CNV calling on two brains with multiple system
atrophy and one control brain using different reference genomes. We found that 38% of brain
cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells
from other brain regions. Our study highlights the importance of selecting whole genome
amplification method and reference genome for CNV calling, while supporting the existence of
somatic CNVs in healthy and diseased human brain.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
2
Introduction
Mosaicism, due to somatic mutations in the human brain, is increasingly recognised, with likely
roles in neurodevelopmental and neurodegenerative diseases 1 2–4. As the ‘signal’ of a low-level
somatic mutation can be lost in ‘bulk’ tissue homogenates, single-cell whole genome sequencing
(scWGS) after whole genome amplification (WGA) has been increasingly utilized in the past
decade 5. Megabase-scale copy number variants (CNVs) can be detected using read-depth
methods even with one or a few million short reads 6–8. Several studies of human single cortical
neurons showed Megabase-scale CNVs, although the precise frequency of these changes
remains unclear 9 7 10 11 12. These CNVs may be more frequent in younger than aged healthy
brains 7,8, arising in embryonic neurogenesis in mouse8, with complex structural variants (SVs)
also arising in human neurogenesis13. We previously performed targeted detection of somatic
CNVs in two related neurodegenerative disorders, Parkinson’s disease (PD) and multiple system
atrophy (MSA). These diseases are summarized under the umbrella term of synucleinopathies,
related to abnormal aggregation of misfolded alpha-synuclein protein, encoded by the gene
SNCA. Using fluorescent in situ hybridisation, we found somatic SNCA CNVs in PD and MSA
brain tissue14 associated with pathology in MSA at the single-cell and regional level15,16.
A range of WGA options exist, such as PCR-based, Multiple Displacement Amplification (MDA,
isothermal, using phi-29, a DNA polymerase with high fidelity and extreme processivity ideal for
amplifying large fragments), and hybrid methods5. Still, one needs to better understand the
advantages and disadvantages/biases across these WGA methods, as it impacts the detection
and interpretation of CNV calls across cells. This is especially needed outside control tissues or
cell lines, as often these do not encapsulate the challenges faced across e.g., brain tissue. In our
previous work16, we used PicoPLEX, a hybrid method related to multiple-annealing and looping-
based amplification cycles (MALBAC), to capture genome-wide somatic CNVs in diseased human
brain tissue for the first time. We detected CNVs in ~30% of cells in two MSA cases in affected
brain regions16, although their relevance remains unclear. We note several previous comparisons
of WGA methods, with the broad consensus being that MDA is not suitable for calling CNVs by
read depth, as there is bias due to over-amplification of certain regions, although it is more
accurate at the single-base level 17–23. There have been several attempts to reduce MDA
amplification bias by performing reactions in nanoliter-scale volumes10,24,25. MDA performed after
the single-cell genome is partitioned into ~50,000 droplets (droplet MDA, or dMDA) in the Samplix
X-Drop device was reported to yield more even amplification26. The recently developed method
of Primary Template-directed Amplification (PTA) also utilizes phi29, but the reaction is terminated
early to avoid over-amplification27. High coverage (>30x) scWGS after PTA allows detection of
single nucleotide variants (SNVs) and indels, already been applied to normal and Alzheimer’s
disease brain28,29, and also shows lower coverage variability than MDA 28.
In this work we provide deeper insights into the advantages and disadvantages of three WGA
approaches currently available commercially (PicoPLEX, PTA, dMDA), the first two of which are
easily scalable. We perform their first direct comparison using human post-mortem frozen brain
samples, including disease brain (two MSA cases) and one control donor. We found that in our
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
3
hands both PicoPLEX and PTA are suitable for CNV calling by shallow WGS, but not dMDA. PTA
amplifies more the human genome, but PicoPLEX is the least noisy method. Furthermore, we
compared chimeras generated by these methods, which could hinder precise SV detection or
introduce other biases in the analysis. We also investigated the utility of alignment to a complete
reference genome (T2T-CHM13) as compared to the commonly used hg38 reference genome,
and liftover back to hg38.
Results and Discussion
To optimize the workflow for detection of large CNVs by scWGS, we compared three different
WGA methods, PicoPLEX, PTA, dMDA. We used single-nuclei isolated from the cortex of up to
three different brains, two with MSA and one control (Fig. 1a). We had previously reported scWGS
from other regions of the same MSA brains16. We also used non-brain control nuclei in selected
experiments: fibroblasts carrying a germline SNCA triplication, and lymphocyte nuclei from
NA12878. In total, we amplified and performed Illumina sequencing (paired-end) across 93 brain
cells, as well as 3 fibroblasts and 5 NA12878 lymphocytes (mean coverage ~0.64x, 0.17x and
0.71x respectively) (Fig. 1b, Supplementary Fig. 1a).
Fig. 1 Experimental overview and scWGS preliminary analysis from human post-mortem
brain samples. a Methodology overview created with BioRender.com (agreement
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
4
KB25LPBTEK). b Mean depth of coverage for each WGA method. PicoPLEX vs PTA ns (adj.
p>0.99), PicoPLEX vs dMDA ** (adj. p=0.008) and PTA vs dMDA * (adj. p=0.04). Kruskal-Wallis
test with Dunn’s multiple comparisons correction. c Bases potentially covered if sequenced
deeper. P value for all pairwise comparisons <0.001. Brown-Forsythe and Welch ANOVA tests
with Dunnett’s T3 multiple comparisons correction. d Median Absolute Deviation (MAD) scores.
P value for all pairwise comparisons <0.001 (adj. p<0.001). Kruskal-Wallis test with Dunn’s
multiple comparisons correction. For b-d PicoPLEX (n=33), PTA (n=21), dMDA (n=39); Mean ±
SD shown.
PTA provides the broadest amplification, but PicoPLEX provides the most even
Although these are “whole” genome amplification methods, some regions may not be amplified in
a given cell (locus dropouts)5. In the absence of high coverage WGS of each cell, the maximum
number of bases which could be retrieved by deeper sequencing can be deduced genome-wide
using Preseq30. We found that PTA provides efficient capture of the genome of brain nuclei,
consistent with other data27,28 (Fig. 1c; mean ± SD: 2.84 ± 0.56 Gb), with PicoPLEX the next best
performer (1.71 ± 0.48 Gb). dMDA provided the lowest breadth of coverage (0.75 ± 0.29 Gb),
even lower than the report of only one-third of the genome covered with 100 million reads after
dMDA26.
CNV calling by read depth is hampered by uneven amplification, as regions which are over- or
under-amplified could appear as CNVs. We compared a key metric, the median absolute
deviation (MAD) between adjacent bins, between the three technologies at 500 kb bin size using
Ginkgo6, after adapting this widely used validated tool to hg38 (see methods). We noted clear
differences across methods, with PicoPLEX performing best and dMDA worst (Fig. 1d, mean ±
SD: PicoPLEX 0.15 ± 0.03, PTA 0.24 ± 0.06, dMDA 0.57 ± 0.07). This is reflected by visual review
of sequencing traces (Fig. 2a), and Lorenz curves, which indicate the amplification variation by
plotting the cumulative fraction of reads as a function of the cumulative fraction of the genome
(Fig. 2b). In MSA1, where we had used different PicoPLEX versions, and different library
preparation for PTA, we verified that the MAD was not affected by this (Supplementary Fig. 1b-
c). Furthermore, the MAD for each WGA method was similar between different brains, and non-
brain samples (Supplementary Fig. 1d-f). The MAD values we obtained for PTA are similar to
the ones reported (~0.25 for bin size range 100-1,000 kb)27. The uneven coverage of dMDA in
particular cannot be explained by GC content variation, although there was a slight drop in
coverage of high GC regions for both dMDA and PTA (Fig. 2c). Indeed, others have reported no
major GC effect on MDA bias23. Although previous reports had shown superiority of PicoPLEX or
MALBAC over MDA for CNV calling18–23,31,32, there had been no prior comparison to dMDA. The
previous study of dMDA, conducted in lymphoblasts, reported more even genome coverage
compared to MDA performed in parallel but not in droplets, although MAD values were not
reported26. As the MAD values we obtained using dMDA were poor, we recalculated MAD after
gradually increasing the bin size up to 5 Mb, which improved the values as expected9. We also
found that MAD can be further improved by removing noise using principal component analysis
(see methods)33 (Supplementary Fig. 2). CNV calling in dMDA might therefore be possible for
very large aberrations after denoising.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
5
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
6
Fig. 2 scWGA method comparison from brain analyzed by Ginkgo at 500 kb bin size. a
Visual view of copy number profiles of single-nuclei amplified by PicoPLEX, PTA and dMDA from
the control (top) and MSA1 (bottom) brains. b Lorenz curves. The black lines with slope 1
represent perfect coverage uniformity. Increasing divergence of the curve of each cell from this
indicates lower overage uniformity. c Effect of GC content in scWGA. For b-c PicoPLEX n=33,
PTA n=21, dMDA n=40.
Each WGA method has propensity for different types of chimeras
All WGA methods can lead to chimeras. These could be misinterpreted as SVs, but also, if they
cluster in certain parts of the genome, could impact CNV calls. We therefore aimed to compare
the frequencies of key types between the three WGA methods (Fig. 3). For this comparison, PTA
cells which had a different library preparation method were analyzed separately, as they had a
divergent profile presumably related to this (Supplementary Fig. 3). In paired-end sequencing,
the read pairs should both be pointing inwards, towards each other. Outward read pairs, indicative
of tandem duplications, were most frequent in PicoPLEX, and least frequent in PTA. This
observation is consistent with a previous report that over half of PicoPLEX artifacts appear as
duplications17. Apparent translocations were most frequent in PTA and least frequent in dMDA.
Other orientations, which include inversions, were most frequent in PTA, and hardly ever seen in
PicoPLEX, but relatively common in dMDA, as previously reported in MDA, where they had
comprised almost all the chimera signatures17.
Figure 3. Comparison of discordant read pairs of brain nuclei amplified with each method.
a Outward pairs. Differences analyzed using Brown-Forsythe and Welch ANOVA test with
Dunnett’s T2 multiple comparisons test. PicoPLEX vs PTA *** (adj. p<0.001), PicoPLEX vs dMDA
*** (adj. p<0.001), PTA vs dMDA * (adj. p=0.02). b Pairs on different chromosomes indicating
translocations. Differences analyzed by Kruskal-Wallis test with Dunn’s multiple comparisons test.
PicoPLEX vs PTA ns (adj. p>0.99), PicoPLEX vs dMDA ** (adj. p=0.002), PTA vs dMDA *** (adj.
p=0.02). c Pairs in other orientations. Differences analyzed by Kruskal-Wallis test with Dunn’s
multiple comparisons test. PicoPLEX vs PTA *** (adj. p<0.001), PicoPLEX vs dMDA *** (adj.
p<0.001), PTA vs dMDA ns (adj. p=0.18. a-c PicoPLEX (n=33), PTA (n=10). dMDA (n=38).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
7
Realignment to T2T-CHM13 and liftover to hg38 affects CNV calling
Next we investigated the impact of the reference genome for the analysis of single-cell CNV. We
realigned all data to the T2T-CHM13 genome34 and performed liftover of the read alignments back
to hg38 using levioSAM2, which improves calling on the original reference, both for small variants,
and for structural variants using long reads.35 We compared relevant metrics between the different
alignments. We found a marginal improvement in the percentage of reads aligned, which was
highest in T2T-CHM13 and partly maintained in hg38 after liftover (Supplementary Fig. 4a). For
both amplification methods, the MAD slightly improved in the T2T-CHM13 and liftover alignments
compared to the original hg38 (Supplementary Fig. 4b; see also methods). The confidence score
was not affected by genome changes (Supplementary Fig. 4c; see also methods). The breadth
of sequencing (bases potentially covered using Preseq) was improved using T2T but was lower
in the liftover genome (Supplementary Fig. 4d).
To determine which brain cells were suitable for CNV calling by Ginkgo, we set strict thresholds.
We discarded cells with MAD score >0.3 as before16, although even higher values have been
considered acceptable8,9. We also calculated the confidence score, which indicates the extent to
which genomic segments have integer copy numbers, rather than intermediate copy number
states which may indicate uneven amplification,11 and retained only cells with confidence score
≥0.7. We used 250 kb bins for PicoPLEX, for consistency with our previous work, which left 23 of
34 cells (Table 1). We initially compared unfiltered PicoPLEX and PTA CNV calls between the
different genome alignments (Supplementary Table 1). T2T-CHM13 alignment followed by
liftover to hg38 led to 65% fewer losses called than hg38. Liftover also increased gains by >10%,
but the T2T-CHM13 gains were even higher (an additional 14%, or 2.15 CNVs per cell). We
reviewed hg38-specific losses and noted that they were mostly shared between cells and
individuals, did not appear robust (as they appeared to be non-integer, intermediate between copy
numbers 1 and 2), and were often around centromeres. The SNCA germline triplication (1.85 Mb),
which we had also used as a positive control before, was detected in all three PicoPLEX-amplified
fibroblasts, including one which failed confidence score filter, regardless of reference used
(Supplementary Fig. 5).
Table 1. Success rates and overview of filtered CNV calls across all brain samples (hg38-
liftover).
Cells passing QC (%)
Cells with at least 1 CNV (%)
CNVs called
PicoPLEX
PTA
PicoPLEX
PTA
Total
Gains
Losses
MSA1
11/15 (73.3%)
5/13 (38.5%)
3
(27.3%)
4
(80%)
26
16
10
MSA2
8/12 (66.7%)
x
3
(37.5%)
x
4
3
1
Control
4/7
(57.1%)
6/12 (50.0%)
1
(25.0%)
2
(33.3%)
6
4
2
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
8
For PTA-amplified cells, we used 500 kb bins, as even at this size only 11 of 25 cells passed our
criteria. We thus decided to focus CNV-calling on the hg38-liftover genomes, limited to
autosomes, as this is better annotated, and the results are easier to compare than T2T-CHM13.
Megabase-scale CNVs are detected in over a third of brain cells
We proceeded to somatic CNV calling in PicoPLEX and PTA-amplified brain cells; the profiles of
all cells and CNVs called are given in Supplementary Fig. 6. To perform stringent filtering, we
first removed all calls that were shared between at least half of the cells, or any cells between
different individuals. To assign a numerical threshold, we calculated the median copy number of
each segment assigned by Ginkgo and plotted the distribution of segments with each copy
number, from which we set copy number thresholds of 1.29 for a loss and 2.80 for a gain (methods
and Supplementary Fig. 7). We confirmed that in males, excluding one cell with possible chrX
aneusomy, CN was below that number in both PicoPLEX and PTA.
This led to a total of 36 CNVs across the three brains, with gains predominant (63.9%; Table 1;
all CNVs listed in Supplementary Table 2, profiles shown in Supplementary Fig. 6), CNVs were
found in 38.2% of cells (13/34 overall; 8/23 PicoPLEX, 6/11 PTA; p=0.46). The proportion of cells
with CNVs were similar in MSA (10/24) and controls (3/10; p=0.7). This is very similar to the ~30%
of cells we estimated to carry somatic CNVs in other regions of the same MSA brains16. For
controls it appears higher than previous estimates in cortical neurons, with 10-25% reported to
carry Mb-scale CNVs7, and more recently only 2 of 52 PTA-amplified cortical neurons carrying
CNVs >~5 Mb 28. In the cells which had CNVs, the mean number per cell was 2.8, although
notably one MSA cell had 17 (11 gains and 6 losses; cell A24; Supplementary Fig. 6).
Examining CNVs further, these were slightly larger in the PTA data, at least partly due to the
higher bin size used (median 5.15 Mb v 3.36 Mb, Mann-Whitney p=0.22; Supplementary Table
2). Gains and losses had similar sizes in PicoPLEX data (3.31 and 3.21 Mb respectively), although
in PTA gains were larger (11.44 v 5.65 Mb). One control brain cell had two distinct losses which
essentially added up to a loss of chromosome 13, with only 2.3 Mb spared, which presumably is
an error, and this chromosome is lost in its entirety. This was the only aneusomy seen in a brain
cell, consistent with estimates of brain aneuploidy of 0.7-5% derived by scWGS1, although one of
three fibroblasts did have a chromosome gain (Supplementary Fig. 5). We noted that 9 brain
CNVs (27.8%) were sub-telomeric. This observation is in agreement with previous work
suggesting enrichment in these regions which are rich in segmental duplications, but considerably
higher than the 9.15% in MSA neurons from other brain regions which we previously reported16.
To understand the nature of genes affected by CNVs, we performed gene ontology analysis using
PANTHER36. We noted divergent enrichment in MSA and control, but due to the sample size we
cannot draw any conclusions (Supplementary Fig. 8).
We also investigated whether CNV calls are supported by another algorithm for scWGS CNV
calling, Copykit,37 also based on circular binary segmentation, which uses hg38 as default
(Supplementary Table 2). The SNCA triplication was detected in all three fibroblasts, although
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
9
the copy number was given as 3 (rather than 4) in the one that failed confidence score. We
allowed for a smaller CNV called by Copykit to be classed as supportive of a CNV called by
Ginkgo if it was encapsulated by the Ginkgo CNV region. The majority of Ginkgo CNV calls in
brain were supported (75.8% for PicoPLEX and 71.4% for PTA). The CNVs in PicoPLEX cells not
reported by Copykit were all <3.5 Mb, except for a centromeric one. The median CNV size was
identical (3.52 Mb PicoPLEX, 3.51 Mb Copykit). For CNVs called by both, Copykit sizes were
slightly smaller (median difference -0.54 Mb), with three notable large CNVs extending to the short
arm telomere being much smaller (by 19-35 Mb) in Copykit. As Copykit has not been formally
benchmarked to our knowledge, while Ginkgo has been found to be accurate in calling
breakpoints38 we gave it the benefit of the doubt for estimating CNV sizes. Ginkgo appears to
have an advantage in detecting relatively small CNVs, and centromeric and telomeric regions.
Some CNVs have support in bulk or other brain regions
If a somatic CNV is limited to a single-cell, orthogonal validation is by definition impossible. A
clonal CNV, however, could be detectable in other cells, from the same or other brain regions. It
may also be detectable by bulk sequencing of adequate depth. This could, however, be
compromised by a number of issues, including imprecise boundaries in single-cell CNV calling,
possible non-amplification of the breakpoints due to allelic drop-out, and the intrinsic limitations in
SV calling if short-read sequencing is used. To investigate this in the two MSA brains, we first
reviewed the presence of CNVs with similar boundaries in previously reported different regions
from the same brains: substantia nigra in both (n=99), and the pons and putamen in MSA1
(n=70)16. One 3.36 Mb gain on chromosome 9 in MSA2 (cell A76; Supplementary Fig. 6) was
essentially identical to a gain which had already been suspected of being clonal, as one
breakpoint had been shared and one located nearby in two cells from the substantia nigra of the
same brain, one neuron and one non-neuron. This includes the TLR4 gene, which encodes a
microglial and astroglial receptor with a role in alpha-synuclein clearance and pathology
propagation39, and is upregulated in MSA40. Although we did not detect a gain of this gene in the
other MSA brain, its presence in a clonal CNV makes further study worthwhile.
To seek support for somatic CNV calls in the MSA brains, we also analyzed deep bulk short-read
WGS from the MSA brains, specifically the cingulate cortex (where the single-cells were obtained
from) and the adjacent cingulate white matter for both cases, as well as the cerebellar cortex and
white matter from MSA2 (mean coverage across all 83.6x; Supplementary Table 3). We used
Samplot41 to visualize any read pairs consistent with each reported CNV in all bam files, scanning
250 kb on each side of Ginkgo-reported breakpoints. We considered any read pair in the WGS
from the same sample and region consistent with the called CNV as tentative support, as long as
it was in a region with good coverage, and, assuming that somatic CNVs would not be shared
between individuals, nothing similar was found in the other brain. We found tentative support (at
least one read pair each) for 4 CNVs, 2 gains and 2 losses, all <10 Mb in size, with support also
in DNA from the adjacent white matter of the same brain in one (Fig. 4; the images of these
genomic regions from all WGS samples are shown in Supplementary Fig. 9).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
10
Fig. 4: Samplot views demonstrating tentative support for single cell CNV calls in bulk
short read WGS. These show all read pairs in the designated regions in cingulate cortex, with
pairs supporting a particular type of CNV / SV indicated according to the scheme at the top, and
those supporting each single-cell CNV arrowed. The left and right panel of each plot show the
regions around the reported proximal and distal breakpoint respectively, which is at the middle of
each panel, with the chromosomal numbers and positions on the x axis below. The y axis indicates
the calculated insert size for the read pairs of interest on the left, and the local coverage on the
right. a-c MSA1 and d MSA2. a duplication 2.47 Mb (cell A24), b deletion 1.58 Mb (cell A24), c
deletion 9.54 Mb (cell L21), d duplication 3.45 Mb (cell A82). A read pair identical to c was found
in the cingulate white matter, suggesting mosaicism across both brain regions.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
11
Putative gains in T2T-CHM13 specific regions may be due to low mapping quality but
merit further assessment
We finally addressed the T2T-CHM13 specific gains (gains called only in the T2T-CHM13
alignments, with at least 50% of their span being novel T2T-CHM13 sequence). Again, we filtered
those shared between >50% of cells or different individuals, to focus on most likely somatic
events. We identified three gains, two involving centromeric active α-sat higher order repeat
(HOR) arrays42. A chromosome 13 acrocentric arm gain had surprisingly high copy numbers (15-
16). We reviewed the copy number of the region covered in all other cells, and we noted that the
PicoPLEX data were much more noisy than PTA in two of these regions (Fig. 5). We investigated
the likelihood of these calls by restraining the mapping quality and assessing how this impacts
the CN estimation. As previously reported, the alignment to the T2T-CHM13 reference results in
less noisy and more reliable alignments for short reads35. Nevertheless, these T2T-CHM13
unique regions have specific challenges due to their repetitiveness34. We filtered the reads based
on mapping quality (MQ) 0, 1 and 10 to assess different stringencies. For MQ1, we observed that
the single copy number gain was removed, but the two high CN gains persisted at MQ1, and were
removed at MQ10. At MQ10, however, many cells had apparent losses in these regions, due to
the poor mappability. This illustrates the difficulty at calling even large CNVs in these regions. We
approached this by filtering more stringently across samples and CN directly, since false positives
should manifest in all samples if it is due to mapping or reference biases. Since this is not the
case in these CN candidates, the possibility of true somatic gains cannot be excluded.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
12
Fig. 5. Evaluation of T2T-CHM13 specific CNV calls using different WGA methods (PTA
and PicoPLEX) and mapping quality filtering. a no mapping quality filter, b mapping quality
1, c mapping quality 10.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
13
Conclusions
Whole genome amplification allows the analysis of single-cell genomes for somatic mutations.
Nowadays there are several available methods to perform this which vary in their advantages
(e.g. evenness of amplification) and disadvantages (e.g. coverage across the genome, scalability,
costs), and even in applicability to neurons, as in the case of single-cell trichannel processing
which requires dividing cells43. In this work, we have directly compared three existing
technologies, the well-established PicoPLEX and two very recently developed adaptations of phi-
29-based isothermal amplification, PTA and dMDA, using human post-mortem brain samples,
and highlighted their characteristics. Furthermore, we have updated the popular single-cell CNV
caller Gingko for hg38, and for the first time assessed the advantage of utilizing alternative
reference genomes besides hg19 or hg38. Indeed, the T2T-CHM13 genome seems to offer
potential novel CNV candidates, although the intrinsically low mappability of the novel regions
complicates detection. Thus, this study provides key insights for experimental considerations for
whole genome single-cell studies and analysis with clear recommendations.
The consistency of amplification across the genome by PicoPLEX makes it preferable for CNV
calling, although PTA can also be used. The latter has the advantage of more complete genome
coverage, albeit at the expense of requiring a larger bin size to allow CNV calling when a strict
MAD cutoff is used. dMDA was not suitable for CNV calling by read depth, but further
developments (e.g., more stringent lysis and increased number of droplets per reaction) could
improve this. Furthermore, the long MDA amplicons make it potentially well suited for long reads
and direct detection of SV breakpoints. This will require effective filtering of pervasive chimeras,
with tools already becoming available44.
Findings of interest in MSA is the apparent clonality of a gain which encompasses TLR4, a
possibly disease-relevant gene39, as well as tentative support for some CNVs by focused analysis
of deep bulk short read WGS. Detection of breakpoints with deep long read WGS and dedicated
analysis for somatic SV’s45 is needed to fully validate these, although breakpoints of CNVs found
in one or a small number of single cells will be impossible to confirm even with sensitive digital
PCR methods. Determining the possible relevance of the TLR4 CNV, and of the apparently high
proportion of brain cells with somatic CNVs in MSA (~40% in the cingulate cortex, and ~30% in
our earlier study of different regions of the same two brains16), will require larger studies with well-
matched controls. Further single-cell genomic studies in sporadic neurodegenerative disorders
are needed to fully elucidate the potential role of somatic mutations in their etiology and
pathogenesis.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
14
Methods
Human Tissue and cell lines
Fresh frozen post-mortem brain samples were provided by the Queen Square Brain Bank,
London, UK. All donors had given informed consent for the use of their brain in research and the
study was approved by the National Research Ethics service London – Hampstead (10/H0729/21)
and from the brain tissue bank by the UK National Research Ethics Service (07/MRE09/72).
Samples from 1 control (frontal cortex) and 2 MSA (cingulate cortex) donors were used in this
study. We selected MSA cases from which we already have scWGS from other regions26.
Demographics are presented in Table 2. As positive control, we used human skin fibroblasts with
a known germline SNCA triplication that we previously used with FISH14 and scWGS. For PTA,
we also used fluorescence-activated nuclei (FANS) sorting NA12878 (B-lymphocyte cell line,
RRID:CVCL_7526) single-nuclei provided by BioSkryB as amplification controls.
Table 2 Demographics of brains used in the study.
Control
MSA1
MSA2
Age of death (years)
91
50
67
Disease duration (years)
NA
2
7
Post-mortem interval
(hours)
38
30
28
Sex
Female
Male
Female
Manual isolation of single-nuclei using CellRaft device
We isolated single-nuclei using the CellRaft system (Cell Microsystems) mounted on a Nikon
Eclipse TE300 inverted microscope coupled to a CCD camera (KERN optics), as described in
detail elsewhere16,18,46. Briefly, we prepared nuclear fractions from 30-50 mg frozen tissues or cell
pellets, and counterstained nuclei with 1 μg/ml DAPI for 20 min on ice. Then, we seeded 5000
nuclei onto a 10,000-raft array pre-treated with Cell-Tak (Corning), and nuclei were allowed to
settle on the raft at 4oC at least overnight. The nuclei were observed under the microscope, and
we selected rafts that contained a single-nucleus with a neuronal appearance (large diameter and
presence of low condensed chromatin). We isolated individual nuclei of interest manually using a
magnetic wand, and subsequently released each nucleus into a 0.2 ml tube containing 5 μl TE or
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
15
Cell Extraction Buffer for PicoPLEX, 3 μl of Cell Buffer for PTA, and in 2.8 μl lysis buffer (200 mM
KOH, 5 mM EDTA pH 8 and 40 mM 1.4 DTT) supplemented with 2 μl dH2O (DNase/RNase-free)
for dMDA and kept them on ice until further use. To avoid cross-contamination, we rinsed the
wand sequentially with DNase I solution (1x, Corning DLW354242), absolute EtOH and dH2O
before and between the individual nuclear collections. In each experiment, we used at least one
negative control (a tube with no raft or a raft with no nucleus) and a positive control (15 pg of bulk
gDNA).
Single-cell Whole Genome Amplification (WGA)
We performed single-cell WGA using the following methods:
1) PicoPLEX Single Cell WGA Kit (Takara, R300672 v2 or R300722 v3) according to the
manufacturer protocol. In brief, lysis mix was added to the samples and lysis reaction carried
out on a thermal cycler. Then pre-amplification reagents were added, and the pre-
amplification reaction was carried out on a thermal cycler. Lastly, amplification mix was added.
including 1x EvaGreen (Biotium) as reporter dye and the reaction was monitored using qPCR
(StepOne, Applied BioSystems). The scWGA products were then cleaned with AMPure XP
beads (Beckman Coulter; 1:1 ratio).
2) PTA using ResolveDNA™ Whole Genome Amplification Kit (BioSkryB PN100136)
according to the manufacturer protocol. All the PTA reagents were added step by step
according to manufactory protocol, but on step 7 the samples were incubated for 20 min at
RT, instead of 10 min in a PCR cooler, for improved lysis. The lysed samples with the enzymes
and terminators were incubated on thermal cycler at 30°C for 10 h before enzyme deactivation
at 65°C for 3 min, followed by bead purification of the amplicons according to the manufacturer
protocol.
3) dMDA kit (Samplix) according to the manufacturer protocol, but with an addition of a lysis step
95ºC for 3 min followed by 10 min cool down at RT) after alkaline lysis of the samples 46.
Briefly, samples underwent alkaline and heat lysis, followed by droplet generation in the
XdropTM instrument (Samplix) which encapsulated the single-cell DNA fragments and dMDA
enzyme mix. The droplets were incubated in a thermal block for 16 h on 30°C before
inactivating the enzyme at 65°C for 10 min. Droplets were then broken by the addition of break
solution and color reagents.
All amplified samples were assessed using Qubit dsDNA BR or HS Assay kits (Thermo Fisher
Scientific) and TapeStation (Agilent) using HS D5000 DNA Tapes or HS D1000 DNA Tapes
(Agilent). All amplicons were stored at -20oC and quantified by Qubit prior to library
preparation.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
16
Library preparation and short-read sequencing
Unless otherwise indicated, scWGA products were manually fragmented using SureSelect XT HS
Enzymatic Fragmentation Kit (Agilent), and libraries for Illumina sequencing were created using
SureSelect XT HS2 DNA Reagent Kit (Agilent) manually or using automation with Agilent Bravo,
according to manufacturer’s guidelines. For PTA, a subset of scWGA products were used without
fragmentation to create libraries using ResolveDNA Library Preparation Kit (BioSkryB) kit,
according to BioSkryB guidelines. Each library was quantified by Qubit dsDNA BR or HS Assay
kits and assessed by TapeStation using D1000 or HS D1000 DNA tapes (Agilent). After quality
control, libraries were pooled together prior to sequencing, and their final concentration
determined using Qubit dsDNA HS Assay Kit, HS D1000 tapes on TapeStation and qPCR
(QuantaBio qPCR Library Quantification or NEBNext Library Quant Kit for Illumina). The pooled
libraries were sequenced on NextSeq 2000 (200 or 300 cycles, Illumina) or NovaSeq SP v1.5
(300 Cycles, Illumina) using paired-end configuration and including 2% PhiX. For three cells (one
dMDA, two PTA), library preparation was repeated and each one sequenced separately, but the
bam files were merged for Preseq, Ginkgo, and Copykit.
Bioinformatic analyses
Read alignment and alignment summary
The sequencing reads from PicoPLEX included a 14 base amplification adapter, and the resulting
fastq files were therefore trimmed using Trimmomatic-v.0.3647. The data were then aligned to
hg38 and T2T-CHM13-v2.0 reference genome with bowtie2-v2.5.148. The sequencing data were
then sorted using Samtools-v.1.1449 and duplicates were marked and coverage metrics collected
using Picard-v2.18.450. BAM files were converted to bed files using Bedtools v2.25.0.51 Liftover of
T2T-CHM13 to hg38 was performed using LevioSAM2-v0.2.2 35 after which duplicates were
marked again. Preseq v.3.1.1 was run on bed files across different genome assemblies using
“gc_extrap” option30. The alignment statistics of all mapping files were generated using Samtools-
v1.14 (see code availability).
Data quality metrics for each amplification method
Data quality assessment and CNV calling were performed using the command-line version of
Ginkgo (https://github.com/robertaboukhalil/ginkgo)6. To allow use of Ginkgo beyond the hg19
genome, we generated variable and constant sized bins for hg38 and T2T-CHM13 using the
buildGenome scripts provided within Ginkgo
(https://github.com/robertaboukhalil/ginkgo/tree/master/genomes/scripts), adapted to run on the
compute infrastructure of the Flemish Supercomputing Center. The data quality for each
amplification method was assessed by calculating MAD, GC content and Lorenz curve with a
variable bin size initially of 500 kb for the hg38 genome. We adapted and ran functions within
Ginkgo to compute locally these three statistics across the autosomes. MAD was calculated
between neighboring bins using normalized read counts (the count of each bin divided by the
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
17
mean read count per cell). As a robust statistic, MAD is resilient to abrupt changes in read counts
resulting from copy number changes. The confidence score was calculated as before. GC
extreme regions could cause read dropout, one of the reasons for uneven coverage. To model
the relationship between normalized read counts and GC content, Ginkgo uses the 'LOWESS'
function in R52, followed by scaling the read counts accordingly. We also computed Lorenz curves
as indicators of uniform amplification for each amplification method. The Lorenz curves and GC
content plots were visually assessed to compare the data quality of PicoPLEX, PTA and dMDA.
Denoising
To reduce high within-cell variation in dMDA data, we applied a principal component analysis
(PCA) based denoising approach. The approach is based on the idea that PCA can capture
common variation in read depth across cells that are mostly likely caused by technical artifacts.
To remove the effect of common variation, multiple regression is applied followed by PCA. CNV
calling can then be performed using the residuals from the regression, instead of using normalized
read counts. Somatic CNVs that are randomly distributed in the genome should remain
unaffected33. We ran Ginkgo for different bin sizes (500 kb, 1 Mb, 2.5 Mb, 5 Mb), and for each bin
size, we removed common variation, starting from 40% and increasing up to 90% in 10%
increments.
CNV calling using Ginkgo
Ginkgo was used for CNV calling across different genome assemblies6. As read lengths varied in
some sequencing experiments, the read length of each dataset was rounded down to the next
smallest value to get more conservative mappability. Ginkgo settings are presented in Table 3
CNV calling. Ginkgo employs circular binary segmentation (CBS), which is implemented in
DNAcopy in R53. DNAcopy (v.1.68.0) was run with Ginkgo-implemented parameters which are:
alpha=0.01, min.width=5. CBS uses a permutation reference distribution to identify the change
points54. To ensure reproducibility, all codes were run with set.seed (1) in R. Independent
segmentation was used throughout. We removed all CNVs smaller than 5 bins.
Table 3. Sequencing read length and corresponding Ginkgo settings.
scWGA method
Read length
Ginkgo read length setting
PTA
58 bp
48 bp
PTA
111 bp
101 bp
PicoPLEX
97 bp
76 bp
PicoPLEX
137 bp
101 bp
dMDA
111 bp
101 bp
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
18
To identify common CNVs shared among multiple cells, we considered two CNVs as shared if
their start and end positions are within 2.5 Mb of each other for PicoPLEX cells, and within 5 Mb
for PTA cells. We removed these calls from downstream analyses, as they could result from either
dry lab/wet lab artifacts or germline mutations. 33,55
Filtering of Ginkgo CNV calls
The output of the segmentation algorithm is integer-like CN values such as 1.2 and 2.1. These
deviations from integer values are due to variations in the data caused by both biological and
technical factors6,7. Therefore, a CN threshold is required to stringently identify gains and losses.
For this, we calculated the median CN of diploid segments across autosomes using PicoPLEX
data, as the number of cells passing QC (MAD ≤0.3 and confidence score ≥0.7) is greater than
that in the PTA data set (25 v 14). The lower limit for the segment size was set to 5, consistent
with our use of a minimum of 5 bins for CNV detection, and the upper limit at the size of the
smallest chromosome (n=127 bins)7. We plotted the distribution of the segments CN values on a
histogram, from which we set thresholds of 1.29 for a loss and 2.80 for a gain (Supplementary
Fig. 7).
CNV calling using Copykit
The newly developed algorithm, Copykit37 was used for comparison against filtered Ginkgo CNV
calls in all cells passing QC. For consistency with Ginkgo parameters used, Alpha was adjusted
to 0.01, and the bin size used for PicoPLEX and PTA amplified cells was 220 kb (as 250 kb is not
available) and 500 kb, respectively. Copykit is compatible with the genome assemblies hg19 and
hg38, therefore CNV calling was performed on hg38 and liftover (not T2T-CHM13). CNV
coordinates were extracted from Copykit using a custom R script.
Short read Illumina bulk WGS analysis
Illumina reads for DNA extracted from both MSA brains were mapped to hg38 using bwa mem
(v.0.7.17-r1188) with default parameters including -M to mark split reads as secondary
alignments. To visualize possible read support for single-cell gains and losses in bulk Illumina
WGS data, we used Samplot (v.1.3.0)41, which creates images that display the read depth and
sequence alignments across specified regions, and reveal any reads supporting a specified CNV.
The coordinates of all filtered CNVs (gains and losses) were input into Samplot, to detect and
reads supporting them in all MSA samples available (2 brain regions from MSA1, 4 regions from
MSA2). High-coverage (250x) HG002 data was used for comparison. We allowed 250 kb on either
side of each reported breakpoint, due to the inherent inaccuracy of defining CNVs using large bin
sizes.
T2T-CHM13 specific gains
To identify the characteristics of T2T-CHM13 specific gains, we used samples that were aligned
to T2T-CHM13 and lifted over to hg38, passing both the MAD (≤0.3) and confidence score (≥0.7)
filters. One PicoPLEX cell ("A14_v2_Exp9_1_sn1_P70_06_CC_S14_R") was excluded from the
analyses due to the absence of a T2T-CHM13 version. The number of cells for PicoPLEX is 25
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
19
and for PTA, it's 14. bedtools subtract -v (v2.30.0)51 was used to identify T2T-CHM13 specific
gains compared to the lifted-over version. T2T-CHM13 unique regions in comparison to hg38
were downloaded from the UCSC Table Browser (http://genome.ucsc.edu/cgi-bin/hgTables,
track: CHM13 unique, table: hub_3671779_hgUniqueHg38 on 2023-06-09)56. If the T2T-CHM13
specific gains for each cell showed a minimum of 50% overlap with the bed file obtained from the
UCSC Table Browser, those positions were kept for the analysis (using Bedtools v2.30.0 with
the intersect -f 0.50 -wo).
Gene Ontology (GO) Analysis
A list of genes covered by significant CNVs was separately identified for MSA patients and
controls using the Ensembl BioMart package57. The gene lists were submitted to PANTHER36 to
identify statistical overrepresentation in any GO categories. The remaining analyses were
conducted as described16,18.
Statistics and reproducibility
Statistical tests were performed, and graphs were plotted using GraphPad Prism (version 10) and
RStudio IDE (v.2023.6.1.524). Normal distribution was assessed using the D'Agostino & Pearson
test. Data are presented as mean ± standard deviation (SD) and a p-value of <0.05 was
considered statistically significant. Comparisons between groups were assessed using Kruskal-
Wallis test with Dunn’s multiple comparisons test, Brown-Forsythe and Welch ANOVA tests with
Dunnett’s T3 multiple comparisons test, paired-matched Friedman test with Dunn’s multiple
comparisons test, Mann–Whitney, unpaired student’s t-test with Welch’s correction, Geisser-
Greenhouse correction and Tukey's multiple comparisons test or RM one-way ANOVA with the
Geisser-Greenhouse correction and Tukey's multiple comparisons test, as indicated in the Figure
legends. All statistical tests were two-sided.
Data availability
All scWGS data will be submitted to the European Genome Archive. Illumina bulk data from the
MSA brains will be submitted to an appropriate repository as part of a larger MSA study. The
Ginkgo bins for hg38 and T2T-CHM13 will be available at Zenodo.
Code availability
Custom scripts written in bash (v.5.1.16) or R (v.4.1.2) using RStudio IDE (v.2023.6.1.524)
(https://rstudio.com/) are available on https://github.com/zgturan/scWGA_comparison.git.
Custom scripts for re-alignment and alignment analysis are available on
https://github.com/srbehera/Brain_scWGA_comparision.
IP rights notice: For the purpose of open access, the author has applied a CC-BY
public copyright license to the Author Accepted Manuscript (AAM) version arising from
this submission.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
20
Author contributions
CP and FJS planned and supervised the study. Single cell wet lab work was performed by EKE
with assistance from DRP. Bioinformatic analysis was done by ZGT with input from IB, SB, CM,
JD, FJS, CP. Statistical analysis was performed by EKE and ZGT. Ginkgo adaptation was
performed by JD. Tissue selection and provision was performed by ZJ. Bulk WGS was provided
by SWS. The first draft was written by EKE, ZGT, CP. All authors contributed to and approved
the final manuscript.
Competing interests
SWS received research support from Cerevel Therapeutics. SWS is a member of the scientific
advisory board of the Lewy Body Dementia Association and the Multiple System Atrophy
Coalition. FJS receives research support from Genentech, Illumina, PacBio and Oxford
Nanopore. All other authors declare no competing interests.
Funding and acknowledgements
The study is funded partly by the joint efforts of The Michael J. Fox Foundation for Parkinson’s
Research (MJFF) and the Aligning Science Across Parkinson’s (ASAP) initiative. MJFF
administers the grant Grant ID 000430] on behalf of ASAP and itself. SB and FS were also
supported by NIH (1UG3NS132105-01,1U01HG011758-01). This research was supported in part
by the Intramural Research Program of the National Institutes of Health (National Institute of
Neurological Disorders and Stroke; project number: 1ZIANS003154). This research was
supported in part by the MSA Trust to CP. CGM is supported by a PhD Fellowship from the MSA
Trust.
We would like to thank all individuals who donated their brains for research, Dr Darlan Mintussi
for help with CopyKit, Dr Jan-Willem Taanman for providing fibroblasts, Thorarrin Blondal for
assistance in optimizing dMDA and UCL Genomics for sequencing support.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
21
Supplementary information
Supplementary Fig. 1 Sequencing coverage of non-brain samples and detailed MAD
comparison between samples and method variations.
a Mean coverage depth distribution of nuclei from cell culture amplified by either PicoPLEX
(fibroblasts with known SNCA triplication isolated by CellRaft, n=3) and PTA (NA12878 cells,
isolated by nuclei sorting, n=5) *; p=0.04 unpaired Mann-Witney test. b-f Median Absolute
Deviation (MAD scores) of nuclei aligned to hg38 at 500 kb bins.
b MAD scores PicoPLEX amplified nuclei from MSA1 donor amplified by either PicoPLEX v2
(Takara R300672, n=10) or PicoPLEX v3 (Takara R300722, n=5) analyzed by unpaired Mann-
Whitney test. v2 vs v3 ns (n=0.95).
c MAD from PTA-amplified brain nuclei when used either SureSelect (Agilent) or ResolveDNA
(BioSkryB) libraries analyzed by unpaired Mann-Whitney test. SureSelect vs ResolveDNA ns
(n=0.33).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
22
d PicoPLEX-amplified nuclei from brain donors (Ctrl n=7, MSA1 n=15, MSA2 n=11) or fibroblast
(n=3) cells analyzed by Kruskal-Wallis test with Dunn’s multiple comparisons test Ctrl vs MSA1
ns (adj. p=0.09), Ctrl vs MSA2 ns (adj. p=0.28), Ctrl vs Fibr ns (adj. p>0.99), MSA1 vs MSA2 ns
(adj. p>0.99), MSA1 vs Fibr ns (adj. p>0.99), MSA2 vs Fibr ns (adj. p>0.99).
e PTA-amplified nuclei from different brain donors (Ctrl n=10), MSA1 n=11) or NA12878 cells
(n=5) analysed by Kruskal-Wallis test with Dunn’s multiple comparisons test. Ctrl vs MSA1 ns
(adj. p>0.99), Ctrl vs NA12878 ns (adj. p=0.88), MSA1 vs NA12878 ns (adj p>0.99).
f MAD of dMDA-amplified from different brain donors (Ctrl n=10), MSA1 n=14, MSA2 n=15)
analysed by Brown-Forsythe and Welch ANOVA tests with Dunnett’s T3 multiple comparisons
tests. All comparisons ns (adj. p>0.99).
Data represent Mean ± SD.
Supplementary Fig. 2 MAD of dMDA at different bin sizes.
The results are shown before denoising (“raw”) and after denoising at different proportions. For
each bin size, we tested the difference between the raw data and after denoising (40%) using the
Wilcoxon test with “paired=TRUE” and alternative=“greater”. For all bin sizes, except for 500 kb
(p=0.06), we found that 40% denoising significantly improved the MAD score: for bin sizes 1 Mb,
2.5 Mb, and 5 Mb, the p-value was < 0.01.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
23
Supplementary Fig. 3 Effect of different library preparations for PTA samples on
discordant read pair orientation.
PTA-amplified cells from MSA1 brain sequenced before either SureSelect (n=6) or ResolveDNA
(n=6) library preparation. Analyzed by Mann–Whitney, unpaired student’s t-test, mean ± SD
shown.
a Outward pairs. SureSelect vs ResolveDNA ** (p=0.002).
b Translocations. SureSelect vs ResolveDNA ** (p=0.002).
c Other. SureSelect vs ResolveDNA ns (p=0.39).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
24
(Continues on the next page)
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
25
Supplementary Fig. 4 Single-cell metrics across different reference genomes.
a % of reads aligned. RM one-way ANOVA with the Geisser-Greenhouse correction and Tukey's
multiple comparisons test. PicoPLEX (n=33): all comparisons *** (p<0.001). PTA (n=21): arrows
indicate library prep; top=ResolveDNA (n=12), bottom=SureSelect (n=9). hg38 vs hg38-lift
***(p<0.001), hg38 vs T2T-CHM23 **(p=0.007), hg38-lift vs T2T-CHM23 ** (p=0.003). dMDA
(n=39) : hg38 vs hg38-lift ns (p=0.14), hg38 vs T2T-CHM23 ns (p=0.27), hg38-lift vs T2T-CHM23
ns (p=0.32).
b MAD. Friedman test with Dunn’s multiple comparisons test. PicoPLEX (n= 33): hg38 vs hg38-
lift ***(p<0.001), hg38 vs T2T-CHM23 ***(p<0.001), hg38-lift vs T2T-CHM23 ns(p=0.42). PTA
(n=20): hg38 vs hg38-lift **(p=0.005), hg38 vs T2T-CHM23 ns(p>0.99), hg38-lift vs T2T-CHM23
*(p=0.03). dMDA (n= 38): hg38 vs hg38-lift *(p=0.02), hg38 vs T2T-CHM23 ***(p<0.001), hg38-
lift vs T2T-CHM23 ns (p=0.26).
c Confidence scores. RM one-way ANOVA with the Geisser-Greenhouse correction and Tukey’s
multiple comparisons test. PicoPLEX (n=33): all comparisons ns, hg38 vs hg38-lift (p=0.98), hg38
vs T2T-CHM23 (p>0.99), hg38-lift vs T2T-CHM23 (p=0.95), PTA (n=20): all comparisons ns, hg38
vs hg38-lift (p>0.99), hg38 vs T2T-CHM23 (p=0.85), hg38-lift vs T2T-CHM23 (p=0.75), dMDA
(n=38): all comparisons ns, hg38 vs hg38-lift (p=0.18), hg38 vs T2T-CHM23 (p=0.92), hg38-lift vs
T2T-CHM23 (p=0.77).
d PreSeq. RM one-way ANOVA with the Geisser-Greenhouse correction and Tukey’s multiple
comparisons test. PicoPLEX (n=33): hg38 vs hg38-lift *(p=0.04), hg38 vs T2T-CHM23
ns(p=0.51), hg38-lift vs T2T-CHM23 ***(p<0.001), PTA (n=20): hg38 vs hg38-lift ns (p=0.06),
hg38 vs T2T-CHM23 *(p=0.05), hg38-lift vs T2T-CHM23 ***(p<0.001), dMDA (n=38): all
comparisons ns, hg38 vs hg38-lift (p=0.11), hg38 vs T2T-CHM23 (p=0.80), hg38-lift vs T2T-
CHM23 (p=0.10).
Data represent Mean ± SD.
Supplementary Fig. 5 SNCA germline triplication (copy number 4) in three single
fibroblasts.
PicoPLEX WGA, 250 kb bins. Arrow points to CNV. Reference genomes from top to bottom: hg38,
liftover, T2T. Note that cell L2 has copy number assigned as 5 in the hg38 and liftover, although
visual inspection indicates that the bin copy numbers are between 4 and 5. Cell L4 fails confidence
score (0.6). Cell L4 also has a chromosome 7 gain.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
26
Supplementary Fig. 6 CN profile of cells passing QC.
Each page shows the profile of one cell, including MAD, confidence score, bin size, and CNV
calls. All CNVs called by Ginkgo are shown in the Table above the CN plot. Filtered CNVs are
highlighted (pink = gain, blue = loss).
(file uploaded separately)
Supplementary Fig. 7 Determination of thresholds for CNV filtering.
Plots show the median CN of segments in cells passing QC. A. PicoPLEX. For losses, we set a
threshold of 1.29 (dotted purple line), as the median CN of chromosome X was lower than that in
all males (n=11). For gains, as the CN 2.8 was found in more segments than the immediately
higher and lower values, we used that as the cut-off (dotted red line). The two segments in red
are the SNCA CNV in the fibroblasts passing QC. B. PTA. As the data were sparse, we applied
the same thresholds as in PicoPLEX, visually shown as purple and red lines. The median CN of
chromosome X in males was below 1.29 in all 5 cells.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
27
(Continues on the next pages)
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
28
(Continues on the next page)
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
29
Supplementary Fig. 8 Gene ontology analysis.
a-b biological process for a control and b MSA brains, c-d molecular function for c control and d
MSA brains. e-f cellular component for e control and f MSA brains.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
30
Supplementary Fig. 9 Samplot visualization of bulk Illumina (ILM) WGS in all brain samples
focusing on genomic regions of single-cell CNVs supported by bulk reads.
All MSA1 and MSA2 brain regions with data available are shown; CingCx= cingulate cortex (also
shown in Fig. 4), CingCx= cingulate white matter, CerCx= cerebellar cortex, CerWM= cerebellar
WM. Read pairs supporting a particular type of CNV / SV indicated according to the scheme at
the top, and those supporting each called single-cell CNV arrowed. The left and right panel of
each plot show the regions around the reported proximal and distal breakpoint respectively, which
is at the middle of each panel, with the chromosomal positions on the x axis below. The y axis
indicates the calculated insert size for the read pairs of interest on the left, and the local coverage
on the right. Note that each supporting read pair is only seen in the cingulate cortex of the MSA
brain where the relevant single-cell was reported, except for c which is also shown in the adjacent
white matter of the same brain.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
31
Supplementary Tables
Supplementary Table 1 CNV calls (unfiltered) for PicoPLEX and PTA data across different
genomes
Gains / cell
Losses / cell
hg38
12.62
11.26
hg38-lift
14.41
4.00
T2T
16.98
3.40
Supplementary Table 2 Information About Significant CNVs:
This shows detailed information about all CNVs which were called after filtering. CN=copy
number. Ginkgo results are shown in columns F-L, and Copykit (if called) L-O. The size
difference for CNV called by both is shown in P.
(File uploaded separately)
Supplementary Table 3 Coverage of bulk WGS
Sample
Cingulate cortex
Cingulate White
matter
Cerebellar
cortex
Cerebellar white
matter
MSA1
85.1
86.3
MSA2
84.1
87.2
77.5
81.3
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
32
Bibliography
1. Leija-Salazar, M., Piette, C. & Proukakis, C. Review: Somatic mutations in
neurodegeneration. Neuropathol. Appl. Neurobiol. 44, 267–285 (2018).
2. Proukakis, C. Somatic mutations in neurodegeneration: An update. Neurobiol. Dis. 144,
105021 (2020).
3. Rohrback, S., Siddoway, B., Liu, C. S. & Chun, J. Genomic mosaicism in the developing
and adult brain. Dev. Neurobiol. 78, 1026–1048 (2018).
4. Bizzotto, S. & Walsh, C. A. Genetic mosaicism in the human brain: from lineage tracing to
neuropsychiatric disorders. Nat. Rev. Neurosci. 23, 275–286 (2022).
5. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the
science. Nat. Rev. Genet. 17, 175–188 (2016).
6. Garvin, T. et al. Interactive analysis and assessment of single-cell copy-number variations.
Nat. Methods 12, 1058–1060 (2015).
7. Chronister, W. D. et al. Neurons with Complex Karyotypes Are Rare in Aged Human
Neocortex. Cell Rep. 26, 825-835.e7 (2019).
8. Rohrback, S. et al. Submegabase copy number variations arise during cerebral cortical
neurogenesis as revealed by single-cell whole-genome sequencing. Proc Natl Acad Sci
USA 115, 10804–10809 (2018).
9. Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number
variation in the human brain. Cell Rep. 8, 1280–1289 (2014).
10. Gole, J. et al. Massively parallel polymerase cloning and genome sequencing of single cells
using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013).
11. McConnell, M. J. et al. Mosaic copy number variation in human neurons. Science 342,
632–637 (2013).
12. Knouse, K. A., Wu, J. & Amon, A. Assessment of megabase-scale somatic copy number
variation using single-cell sequencing. Genome Res. 26, 376–384 (2016).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
33
13. Sekar, S. et al. Complex mosaic structural variations in human fetal brains. Genome Res.
30, 1695–1704 (2020).
14. Mokretar, K. et al. Somatic copy number gains of α-synuclein (SNCA) in Parkinson’s
disease and multiple system atrophy brains. Brain 141, 2419–2431 (2018).
15. Garcia-Segura, M. E., Perez-Rodriguez, D., Chambers, D., Jaunmuktane, Z. & Proukakis,
C. Somatic SNCA copy number variants in multiple system atrophy are related to pathology
and inclusions. Mov. Disord. 38, 338–342 (2023).
16. Perez-Rodriguez, D. et al. Investigation of somatic CNVs in brains of synucleinopathy
cases using targeted SNCA analysis and single cell sequencing. Acta Neuropathol.
Commun. 7, 219 (2019).
17. Voet, T. et al. Single-cell paired-end genome sequencing reveals structural variation per
cell cycle. Nucleic Acids Res. 41, 6119–6138 (2013).
18. Perez-Rodriguez, D., Kalyva, M., Santucci, C. & Proukakis, C. Somatic CNV Detection by
Single-Cell Whole-Genome Sequencing in Postmortem Human Brain. Methods Mol. Biol.
2561, 205–230 (2023).
19. de Bourcy, C. F. A. et al. A quantitative comparison of single-cell whole genome
amplification methods. PLoS ONE 9, e105585 (2014).
20. Chen, M. et al. Comparison of multiple displacement amplification (MDA) and multiple
annealing and looping-based amplification cycles (MALBAC) in single-cell sequencing.
PLoS ONE 9, e114520 (2014).
21. Babayan, A. et al. Comparative study of whole genome amplification and next generation
sequencing performance of single cancer cells. Oncotarget 8, 56066–56080 (2017).
22. Hou, Y. et al. Comparison of variations detection between whole-genome amplification
methods used in single-cell resequencing. Gigascience 4, 37 (2015).
23. Ning, L. et al. Quantitative assessment of single-cell whole genome amplification methods
for detecting copy number variation using hippocampal neurons. Sci. Rep. 5, 11415 (2015).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
34
24. Marcy, Y. et al. Nanoliter reactors improve multiple displacement amplification of genomes
from single cells. PLoS Genet. 3, 1702–1708 (2007).
25. Leung, K. et al. Robust high-performance nanoliter-volume single-cell multiple
displacement amplification on planar substrates. Proc Natl Acad Sci USA 113, 8484–8489
(2016).
26. Hård, J. et al. Long-read whole genome analysis of human single cells. BioRxiv (2021)
doi:10.1101/2021.04.13.439527.
27. Gonzalez-Pena, V. et al. Accurate genomic variant detection in single cells with primary
template-directed amplification. Proc Natl Acad Sci USA 118, (2021).
28. Luquette, L. J. et al. Single-cell genome sequencing of human neurons identifies somatic
point mutation and indel enrichment in regulatory elements. Nat. Genet. 54, 1564–1571
(2022).
29. Miller, M. B. et al. Somatic genomic changes in single Alzheimer’s disease neurons. Nature
604, 714–722 (2022).
30. Daley, T. & Smith, A. D. Modeling genome coverage in single-cell sequencing.
Bioinformatics 30, 3159–3165 (2014).
31. Zhou, X. et al. Comparison of Multiple Displacement Amplification (MDA) and Multiple
Annealing and Looping-Based Amplification Cycles (MALBAC) in Limited DNA Sequencing
Based on Tube and Droplet. Micromachines (Basel) 11, (2020).
32. Zhang, C.-Z. et al. Calibrating genomic and allelic coverage bias in single-cell sequencing.
Nat. Commun. 6, 6822 (2015).
33. Turan, Z. G. et al. Somatic copy number variant load in neurons of healthy controls and
Alzheimer’s disease patients. Acta Neuropathol. Commun. 10, 175 (2022).
34. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).
35. Chen, N.-C. et al. Improved sequence mapping using a complete reference genome and
lift-over. BioRxiv (2022) doi:10.1101/2022.04.27.489683.
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
35
36. Mi, H. et al. Protocol Update for large-scale genome and gene function analysis with the
PANTHER classification system (v.14.0). Nat. Protoc. 14, 703–721 (2019).
37. Minussi, D. C. et al. Resolving clonal substructure from single cell genomic data using
CopyKit. BioRxiv (2022) doi:10.1101/2022.03.09.483497.
38. Mallory, X. F., Edrisi, M., Navin, N. & Nakhleh, L. Assessing the performance of methods
for copy number aberration detection from single-cell DNA sequencing data. PLoS Comput.
Biol. 16, e1008012 (2020).
39. Venezia, S., Kaufmann, W. A., Wenning, G. K. & Stefanova, N. Toll-like receptor 4
deficiency facilitates α-synuclein propagation and neurodegeneration in a mouse model of
prodromal Parkinson’s disease. Parkinsonism Relat. Disord. 91, 59–65 (2021).
40. Brudek, T., Winge, K., Agander, T. K. & Pakkenberg, B. Screening of Toll-like receptors
expression in multiple system atrophy brains. Neurochem. Res. 38, 1252–1259 (2013).
41. Belyeu, J. R. et al. Samplot: a platform for structural variant visual validation and automated
filtering. Genome Biol. 22, 161 (2021).
42. Altemose, N. et al. Complete genomic and epigenetic maps of human centromeres.
Science 376, eabl4178 (2022).
43. Sanders, A. D. et al. Single-cell analysis of structural variations and complex
rearrangements with tri-channel processing. Nat. Biotechnol. 38, 343–354 (2020).
44. Lu, N. et al. 3rd -ChimeraMiner: A pipeline for integrated analysis of whole genome
amplification generated chimeric sequences using long-read sequencing. BioRxiv (2022)
doi:10.1101/2022.08.13.503872.
45. Smolka, M. et al. Comprehensive Structural Variant Detection: From Mosaic to Population-
Level. BioRxiv (2022) doi:10.1101/2022.04.04.487055.
46. Kalef-Ezra, E., Perez-Rodriguez, D. & Proukakis, C. Manual isolation of nuclei from human
brain using CellRaft device and single nucleus Whole Genome Amplifica... (2023).
47. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
36
sequence data. Bioinformatics 30, 2114–2120 (2014).
48. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods
9, 357–359 (2012).
49. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–
2079 (2009).
50. Picard Tools - By Broad Institute. http://broadinstitute.github.io/picard/.
51. Quinlan, A. R. BEDTools: The Swiss-Army Tool for Genome Feature Analysis. Curr.
Protoc. Bioinformatics 47, 11.12.1-34 (2014).
52. Cleveland, W. S. LOWESS: A program for smoothing scatterplots by robust locally
weighted regression. The American Statistician 35, 54 (1981).
53. Seshan, V. E. & Olshen, A. DNAcopy: DNA copy number data analysis. R package version
1.68.0. (2021).
54. Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for
the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).
55. Mallory, X. F., Edrisi, M., Navin, N. & Nakhleh, L. Methods for copy number aberration
detection from single-cell DNA-sequencing data. Genome Biol. 21, 208 (2020).
56. Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32,
D493-6 (2004).
57. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases
and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).
.CC-BY 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted August 8, 2023. ; https://doi.org/10.1101/2023.08.07.552289doi: bioRxiv preprint
