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RESEARCH ARTICLE

# Wet-lab preparation and automated dataset decontamination procedure for bacterial single-cell genomics

Solène Mauger<fnref n="1" /> ID | Yann Sevellec<fnref n="1" /> | Léna Carret<fnref n="2" /> | Noé Robert<fnref n="1" /> | Cecile Monard<fnref n="1" /> |
Cécile Thion<fnref n="2" /> | Laura Jarassier<fnref n="2" /> | Philippe Vandenkoornhuyse<fnref n="1" /> ID

**Correspondence**

Philippe Vandenkoornhuyse
Email: philippe.vandenkoornhuyse@universennes.fr

**Funding information**

Fourth Framework Programme, Grant/
Award Number: 101131818; Agence
Nationale de la Recherche, LabCom,
Grant/Award Number: 21-LCV1-0003-01

Handling Editor: James Alexander Orr

**Abstract**

1. Applying single-cell genomics (SCG) to microbes is an excellent opportunity to study their genome structure, diversity and evolution. The interpretation of SCG datasets depends directly on the quality of sample preparation and data processing, which should be as universal as possible to allow comparison and synthesis of the outcomes.

2. We propose an adaptable and universal SCG library preparation protocol and an automated data decontamination tool, which we named SINgle-CEll REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) workflow. Our workflow identifies and removes contaminant reads based on (i) their contribution to high-coverage regions of the assembly and (ii) their taxonomy at both read and contig levels. We applied these workflows to single cells isolated from laboratory strains and published SCG environmental datasets, demonstrating the procedure's efficiency in cleaning datasets.

3. We highlight sources of contamination and sample preparation biases that jeopardise the quality of recovered single-cell genomes. Using SINCERE-DATA, we successfully removed contaminant DNA sequences from single-amplified genomes that were undetected with the currently available decontamination tools used.

4. In this work, we propose the full workflow embedding bacterial single-cell library preparation and a dedicated bioinformatic tool, SINCERE-DATA, to produce automatically decontaminated single-amplified genomes. This workflow constitutes a new attempt to unify and improve the quality and comparison of the single-cell datasets that will allow standardised examination of data contamination and provide cleaner genomes from the uncultivated majority.

**KEYWORDS**

bioinformatics, microbial ecology, sequencing data decontamination, single-cell omics

<footnote n="1">¹Université de Rennes, UMR 6553
ECOBIO, Rennes, France</footnote>

<footnote n="2">²Cellenion, Lyon, France</footnote>
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# 1 | INTRODUCTION

The use of single-cell omics in microbiology started almost two decades ago (Raghunathan et al., 2005) and advanced our understanding of bacterial interactions, population diversification and evolutionary dynamics within natural bacterial communities (Bawn et al., 2022; Davis & Isberg, 2016; Kashtan et al., 2014). Compared with meta-omics approaches, the strongest argument for single-cell omics is avoiding chimera reconstruction and distinguishing genetic variants within bacterial populations. Distinguishing contaminant DNA from target DNA is therefore crucial to obtain the purest possible genomes. However, regardless of the procedure used for sample preparation, single-cell omics still struggles to reconstitute large genome assemblies and is still subject to contamination (Anstett et al., 2023; Chen et al., 2017; López-Escardó et al., 2017). To date, efforts to reduce contamination have been concentrated on working in cleaner environments and limiting the reaction volumes, which simultaneously limit costs (Blainey, 2013; Mauger et al., 2022). Besides reducing contamination, lower reaction volumes make it possible to increase the quantity of cells studied thanks to a significant reduction in cost per cell, plus improve the accuracy of the bacterial community representation. Currently, commercial solutions for sample multiplexing propose a maximum of 384 unique primer indexes (i.e. NexteraXT Illumina, QIAseq FX DNA Library Kit; QIAGEN), thereby limiting the number of cells processed in the same sequencing run to 384, consequently indirectly increasing the cost of single-cell genomics (SCG) for a larger number of cells. To facilitate the high throughput of SCG, increased multiplexing possibilities need to be addressed.

Diverse forms of contamination of single-amplified genomes (SAG) datasets are possible from biological, technical or bioinformatic sources but are not systematically detected, resulting in distorted genomic information in databases (Cornet & Baurain, 2022; Eisenhofer et al., 2019; Goig et al., 2020; Vollmers et al., 2022). Universal and efficient decontamination of SCG datasets is therefore difficult to achieve, and only, a few published studies have attempted in-depth decontamination of SAGs (Anstett et al., 2023; Bowers, Doud, & Woyke, 2017; López-Escardó et al., 2017). The most widely used decontamination procedures rely mainly on the taxonomic assignation of contigs or marker genes that are selected and removed from the final assembly using different tools, such as Anvio's (Eren et al., 2015), ProDeGe (Tennessen et al., 2015), acdc (Lux et al., 2016), MDMcleaner (Vollmers et al., 2022) and MAGpurify (Nayfach et al., 2019). Decontamination

tools have been proposed for manual contamination detection and decontamination based on taxonomic classification of reads (Dotan et al., 2023). However, the procedure is time-consuming in the case of large datasets and lacks consistency. To date, no workflow proposes automated read decontamination of SCG datasets to avoid misassemblies or the creation of chimeric contigs.

In the present study, we modified and optimised existing library preparation procedures in published or commercial approaches to reduce the reaction volumes and increase the multiplexing options (Figure S1). We applied our protocol to referenced strains of bacteria with fully sequenced, annotated and publicly available genomes in order to evaluate the quality of the recovered genomes. We developed a miniaturised protocol with reaction volumes reduced to a few microliters and designed barcoded primer adapters to identify up to 37,000 cells independently. The genome amplification methods available for bacteria, the most widely used being multiple displacement amplification, tend to over amplify small, non-target DNA (Sabina & Leamon, 2015) and contaminant sequences with lower GC% than the target sample sequences (Jamal et al., 2021). Both these artefacts created by MDA and contaminant environmental DNA have been estimated to account for up to 70% of amplified DNA (Raghunathan et al., 2005), an observation confirmed in several studies (Cornet & Baurain, 2022; Olomu et al., 2020). The free DNA contaminants can be contained in the sample and/or can be caused (1) by DNA traces from various kits used for sample preparation (i.e. kit-ome) (Grahn et al., 2003; Mohammadi et al., 2005), which in this case are expected across samples, and (2) by well-to-well contamination during sample handling (i.e. splashhome) (Eisenhofer et al., 2019; Olomu et al., 2020) where, if existing, are expected to be present in multiple random wells in a single experiment. Amplification of contaminant sequences with lower GC% may also be favoured over high GC% sequences in samples with a low concentration of DNA in single-cell omics applications (Jamal et al., 2021). As a result, loci that exhibit high coverage are frequently the result of biased amplification towards contaminant and small fragment DNA (Gollnisch et al., 2023). Our automated bioinformatics workflow named SINgle-CEll REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) for SCG data treatment can systematically identify contaminants based on different contigs coverage between target DNA and contaminant DNA and screen the dataset for recurring contaminants based on their taxonomic affiliation (Figure 1). We applied our workflow to 500 SAGs from three published datasets (Aoki et al., 2022; Berube et al., 2018;

**FIGURE 1** Main steps of the automatic decontamination workflow SINgle-CEll REads Decontamination through Automatic Taxonomic Assignation. (a) From trimmed reads, the main taxa in each single-amplified genome (SAG) dataset are identified before being assembled (here green for 11 samples and purple for one sample). (b) Reads affiliated to deviant coverage regions (i.e. abnormally high coverage) on the assembly were selected for each SAG dataset and affiliated to a major taxonomy. If this taxonomy differed from the main taxonomy of the SAG concerned, the 'deviant coverage region' was considered to be an 'outlier region' and a candidate contaminant (in purple in this example). (c) The outlier regions of each SAG compared with the total collection of the outlier regions: If present in more than 20% of the SAGs (i.e. 'outlier prevalence' threshold) and their taxonomic affiliation corresponds to less than a user-defined threshold of the main taxonomy of all the SAGs (i.e. 'sample taxonomy' threshold, see Figure 2a for this threshold determination), they were confirmed contaminants. In this example, the purple taxon is detected as an outlier on more than 20% of the samples and present as the main taxon in less than 10% of the dataset and flagged as contaminant. (d) The reads affiliated with these contaminants (in purple here) were removed from the dataset, the assembly was performed a second time and the contigs with the same taxonomy as the previously detected contaminants were removed. Created in https://BioRender.com.
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SINCEREDATA

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Kawano-Sugaya et al., 2024) and, as a result, emphasise the importance of decontaminating SAGs prior to the final assembly stage.

## 2 | MATERIALS AND METHODS

### 2.1 | Preparation of the cells

Two strains of bacteria (*Pseudomonas fluorescens* ATCC® 13525 and *Staphylococcus epidermidis* ATCC® 12228) were grown in nutrient broth (Merck). *Pseudomonas fluorescens* was grown at 28°C and *S. epidermidis* at 37°C. The genomic DNA of *Bacillus subtilis* and *Micrococcus luteus* was extracted using the GenElute™ Bacterial Genomic DNA Kit (Merck) and used as positive controls. For single-cell isolation, the equivalent of 1.10⁷ cells of exponential phase bacterial cultures was collected by centrifugation for 3 min at 9000rcf; the supernatant was removed and the cell pellet was resuspended in equal volumes of sterile PBS. This step was repeated once. The cell suspensions were diluted to reach an approximate final concentration of 1.10⁵ cells per mL in PBS.

### 2.2 | Single-bacteria isolation and lysis

The protocol was built using an innovative cell isolation tool named cellenONE (Cellenion), which allowed the isolation of bacteria based on automatic optical detection. The cellenONE glass Piezo Dispense Capillary (PDC) can handle liquids in small volumes (i.e. 150–800pL per drop, with very high precision), thereby limiting the risk of external DNA contamination. The cellenONE X1 BSC model was placed in a Class II Biosafety Cabinet (BSC) to protect experimental staff from biohazards as well as to prevent external contamination of the samples. The temperature of the platform holding the samples was kept at 4°C to ensure reagent stability. Between the handling of different strains, the PDC used for cell suspension and isolation was systematically sterilised by successive aspiration and immersions in 0.5% chlorine, 3% hydrogen peroxide and 70% ethanol for 2 min each, then ultrasonication following the manufacturer's instructions. The detection and isolation parameters were based on size and elongation measured on brightfield images using the microLIFE package in the cellenONE software, which is specifically designed for the isolation of small particles. Quality control analysis of isolation runs was performed using the microLIFE viewer, and single-cell accuracy (i.e. the proportion of the positions in which only a single cell was dispensed) was 89%. Positions with isolation errors (doublets or empty droplets due to false detection) were recorded, and the corresponding cells were not conserved for library preparation.

In preliminary tests, 12 candidate lysis buffers were tested to satisfy the need for both the absence of inhibition of subsequent molecular reactions and efficient breaking of the cell walls of the different bacteria (*Escherichia coli*, *B. subtilis*, *M. luteus*). The best candidate lysis buffer was adapted from Stepanauskas et al. (2017)

who applied an alkaline treatment to isolate soil bacteria in order to break down their microbial cell walls. The lysis buffer comprised 100mM DTT, 10mM EDTA and 0.4M KOH. A three-quarter dilution of the lysis buffer in nuclease-free water (NFW) (i.e. 0.45µL of buffer plus 0.6µL NFW) was distributed manually in the wells of a 384-well plate under a PCR flow hood. Out of 274 wells used, 123 received no cells to serve as negative controls for sample preparation, 22 received genomic DNA as positive controls and 129 received a single cell. Genomic DNA from *B. subtilis* and *M. luteus* was distributed manually as positive controls for a final 100pg input of gDNA in the reaction (i.e. 0.2µL in each well at 0.5ng/µL). Next, the plate was centrifuged for 2 min at 2000rcf to ensure the cells were properly deposited in the lysis buffer and then placed on ice for 10 min. To stop the lysis and to re-adjust the pH of this alkaline buffer, 0.5µL of 1M Tris-HCl pH4 was added to each well. After being centrifuged for 2 min at 2000rcf, the plates were left overnight at -20°C.

### 2.3 | gDNA amplification

The following day, the plate was thawed on ice, centrifuged for 2 min at 2000rcf, and manipulated under the BSC. For the genome amplification, either the REPLI-g Advanced DNA Single-cell kit (QIAGEN) or a mix of NEB Phi 29 enzyme and reaction buffer was used. We used reagents from the two suppliers to evaluate potential sources of contamination and the viability of the approaches. In both cases, 6µL of a mixture consisting of 4.35µL of reaction buffer, 1.35µL of NFW and 0.3µL of enzyme for the QIAGEN Kit, and 0.75µL of reaction buffer, 2.53µL of NFW, 0.75µL dNTP at 10mM, 1.89µL of random primers at 200µM and 0.08µL of enzyme for the NEB reagents were added in each well. The MDA was performed for 4h (QIAGEN products) or 8h (NEB products) at 30°C with a 3-min final step in a Thermal Cycler at 65°C. Each amplification was quantified using Picogreen (Invitrogen) green fluorescence with qPCR cycler (BioRad CFX96), after which amplified DNA was normalised at 5 ng/µL in NFW and stored at -20°C until further use.

### 2.4 | Enzymatic fragmentation

The libraries were prepared using the QIAseq FX DNA Library Kit from QIAGEN or NEBNext Ultra II FS DNA Library Prep Kit from NEB. The results obtained using the two kits were similar. Two microlitres of normalised MDA products (i.e. 10 ng of DNA) were placed in the wells of a new 384-well plate, and 3µL of fragmentation mix were added. The mix consisted of either 1.5µL of NFW, 0.5µL of fragmentation buffer and 1µL of enzyme for the QIAGEN kit, or 1µL of NFW, 1µL of fragmentation buffer and 1µL of the enzyme (diluted 11.6 times) for the NEB reactions. The tubes and plates were kept on ice during handling to prevent enzyme activity. The plate was then placed in a thermocycler previously set at 4°C, and the fragmentation cycle ran for 12 min at 32°C, followed by 30 min at 65°C to stop enzyme activity.
## 2.5 | Adapter ligation

For this study, we designed 20 adapters (12 in columns and eight in rows) with eight base-pair specific barcodes to tag and differentiate 96 cells with combinatorial barcoding (Figure S1; Table S1). The pairs of barcodes were attributed four times to cover the 384 wells. Each adapter was added in each well of new 96-well plates, 0.5 µL in QIAGEN wells or 0.2 µL in NEB wells, at 10 µM. The ligation mix (4.5 µL for QIAGEN or 5.1 µL for NEB) was added to the 5 µL of fragmented DNA. For QIAGEN reagents, the composition of the mix was 2 µL of buffer, 1 µL of enzyme and 1.5 µL of NFW. For NEB reagents, the mix contained 4.6 µL of ligation master mix, 0.2 µL of enhancer and 0.3 µL of NFW. The plates were incubated at 20°C for 15 min. The ligation products of each set of 96 pairs of barcodes were pooled in one tube and immediately purified with magnetic SPRI beads (Bagnoli et al., 2018) at 0.8x the first time and at 1x the second time. The samples were stored at 4°C until further use.

## 2.6 | Indexing PCR and sequencing

The final PCR was performed with the NEBNext® Ultra™ II Q5® Master Mix and standard Illumina index primers with double indexing (IDT-DNA). For each sample, 1.25 µL of each primer, 5 µL of sample, 5 µL of NFW and 12.5 µL of Master mix were used. The libraries were sequenced on a Nextseq 2000 sequencer with a P2 600 cycles flow cell (Illumina), providing an average Q30 of 88.18% and 374.47 million reads.

## 2.7 | Sequence data treatment

The demultiplexing of the dual indices was performed with cutadapt (V. 4.1) with an error rate of 15% (Martin, 2011). Adapter removal and quality trimming were also performed using cutadapt (V. 4.1) with minimum quality set at 15 and minimum length at 30 bp. The quality of the trimmed reads was evaluated with fastqc (V. 0.11.5; Babraham Bioinformatics–FastQC a Quality Control tool for High Throughput Sequence Data, n.d.). Before decontamination, 30 SAGs were selected from the whole dataset, based on their estimated assembly quality (i.e. assembly length >35 Kb).

An automated workflow, SINCERE-DATA (Figure 1), was developed to identify and remove contaminants based on assembly coverage and on the taxonomic affiliation of reads. Trimmed reads were first assembled with SPAdes (V. 4.0.0; Bankevich et al., 2012) using the careful option, which adds a mismatch and short indel correction step during the assembly, and single-cell options that account for highly uneven coverage and for amplification artefacts (--careful -sc). Contigs shorter than 500 bp were considered as 'noise' and removed using reformat.sh from the bbtools suite (V. 39.0.0; Bushnell et al., 2017). For each sample, the most abundant taxa were determined from the sequencing reads using Kraken 2.

To consider 'kit-ome-' and 'splash-ome-' type contamination, the SINCERE-DATA workflow was implemented with an optional script, available on the SINCERE-DATA GitHub, (Splashome finder.py) for identifying the prevalent taxonomy in the reads of a given dataset, followed by an alert when a taxon exceeds the user-defined proportion of the SAGs. As the proportion can vary with the dataset, as a starting point, we propose screening for taxa represented in more than 70% of the SAGs in a diverse collection. This option can be turned off when working with pure strains or with a uniform dataset. Other potential contaminant regions in the assemblies were detected from abnormally high measured coverages, hereafter termed 'deviant coverage regions' (Figures 1 and 2b). The regions were detected via the following three steps: (1) alignment of the reads on the assembly with Bowtie 2 (V. 2.5.4; Langmead & Salzberg, 2012), (2) sorting and indexing of the alignments using SAMtools (V. 1.1.21; Danecek et al., 2021) and (3) evaluation of the coverage with bedtools (V. 2.31.1; Quinlan & Hall, 2010). The z-score was calculated to evaluate the distance from the standard deviation measured: both the nucleotide position and the regions that presented coverage exceeding a z-score of 2, that is twice the standard deviation above the mean for more than 30 base pairs, were predicted as a deviant coverage region. The predicted deviant coverage regions located at a distance of less than 100 bp from each other on the assembly were merged into a larger region to reduce the computational footprint. The reads mapped on these regions were extracted using SAMtools and taxonomically classified using Kraken 2. Reads with similar taxonomic affiliations to those of the majority of the assembly were left untouched (Figures 1 and 2b). Conversely, if the taxonomic affiliation of the deviant coverage region differed from the affiliations of the majority of the assembly, the associated sequences were put aside and characterised as 'outlier regions'. The outlier regions were collected and compared across the entire dataset to assess their prevalence in the whole collection of SAGs and the likelihood that these outlier regions originated from environmental or artificial contaminant DNA (Figures 1 and 2b). SINCERE-DATA uses two user-defined thresholds to examine the contaminant potential of outlier regions: (1) the 'outlier prevalence' threshold, which determines the proportion of SAGs (i.e. as a percentage of the dataset) containing a given outlier region and (2) the 'sample taxonomy' threshold, which defines the proportion of SAGs (i.e. the percentage of the dataset) with a main taxon identical to a detected potential contaminant in the outlier regions. Outlier regions both above the 'outlier prevalence threshold' and below the 'sample taxonomy threshold' were validated as contaminants and their associated DNA sequences (i.e. the sequencing reads of the contaminant and associated inferior taxonomic ranks) were removed from the whole dataset using KrakenTools (https://github.com/jenniferlu717/KrakenTools). Based on the results of previous tests of SINCERE-DATA on 384 genomes from our lab strains dataset, the optimal value for the 'outlier prevalence threshold' was determined to be 20% to remove the maximum amount of contamination while conserving as much of the
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BRITISH
ECOLOGICAL
SOCIETY

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FIGURE 2 Development and optimisation of SINGle-CEll REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) workflow. (a) The sample threshold (i.e. the proportion of outlier regions with identical taxonomic affiliation of n % of the single-amplified genome [SAGs]) can be chosen by the users based on different criteria related to the dataset quality: (i) The number of SAGs detected contaminated and deleted after the decontamination at various thresholds. (ii) The proportion of contigs contained in each assembly that was assigned to the targeted cell. (iii) The assembly length in Kb and finally (iv) the putative completeness and (v) the putative contamination in percentage measured by CheckM. The vertical dashed line is set to a threshold of 0.1% to represent the threshold we chose for the three studied datasets. (b) The abnormally high covered regions of the assembly can either belong to the target cell (Peak 1) or contain segments of contaminant sequences that will be identified and removed by SINCERE-DATA (Peak 27). This particular assembly is part of the ocean dataset, SAG AG-312-B15, with a main taxonomy identified as *Prochlorococcus*. Created in https://BioRender.com.

targeted DNA as possible. More importantly, regarding the 'sample
taxonomy threshold', we performed a thorough analysis test from
published datasets to assess the impact of the chosen threshold
on the dataset quality metrics (Figure 2a). Here, we applied the
same sample threshold of 0.1% on all environmental datasets, but
a custom choice is possible if the user wants to prioritise certain
metrics rather than others. We suggest raising the 'sample taxon-
omy threshold' from 0.1% to 20% or if the contaminant DNA load
is high. SINCERE-DATA also includes a blacklist feature to be filled
in by the user to automatically remove known contaminants or an
unwanted taxon. We recommend using the main taxa detected
within the negative controls to specify the taxon to be excluded.
In this study, Eukaryotes, Enterobacteriaceae and *Salasvirus* were
added to the taxon blacklist based on the negative control taxo-
nomic affiliation (Figure S3) (See Section 3). After these first de-
contamination steps at the sequencing read level, final assemblies
were performed for each decontaminated dataset using SPAdes
with the same parameters as above. Contigs smaller than 500 bp
were also removed from the assembly. A final curation of contigs
based on their taxonomic classification was performed to get rid of
any potential remaining contaminant contigs assembled from pre-
viously conserved unclassified reads. If identical to the previously
identified contaminants, the contigs were removed manually using
KrakenTools. As an option, the filtered contigs can be saved and a
search for mobile elements, such as plasmids using Platon (V.1.7;
Schwengers et al., 2020) to include them back into the cleaned
assembly. The detection of other mobile elements within SAGs, in-
cluding prophage detection, is planned for future SINCERE-DATA
releases. The quality of the cleaned assembly was estimated with
QUAST (V.502; Gurevich et al., 2013) with default parameters and
CheckM (V.1.2.0; Parks et al., 2015) using 'lineage' workflow to
estimate the putative completeness and contamination of SAGs.
Finally, Kraken 2 was used to determine the taxonomic assign-
ment of the final cleaned SAGs as well as the contigs removed by
SINCERE-DATA (Figure 1). This workflow is available from GitHub
https://github.com/ysevel/SINCERE_DATA. A ZENODO archive
at https://doi.org/10.5281/zenodo.17104423 is also available as
well as a precompiled online version on Lifewatch (https://my.lifew-
atch.eu/workflow/create-workflow-from-template/sincere-data).

The SINCERE-DATA workflow was tested on three published en-
vironmental datasets: (1) A study aimed at referencing uncultivated
genomes of marine microbes (Berube et al., 2018) from which we

randomly selected 100 SAGs, (2) 200 SAGs from the rice rhizosphere
microbiome (Aoki et al., 2022) from the sequencing plate 'COI_15'
and (3) 200 SAGs from oral and faecal human microbiome (Kawano-
Sugaya et al., 2024) from the sequencing plate 'QLF001sag'. The
metadata of the chosen SAGs is in Table S3. We used Anvi'l'o (Eren
et al., 2015) to detect the presence of ribosomal genes required by
the Genomic Standard Consortium (Bowers, Kyrpides, et al., 2017)
to classify the assemblies into the 'low', 'medium' or 'high'-quality
categories. The SINCERE-DATA workflow was then applied sepa-
rately to these three datasets, both using the reads mapped on the
published assemblies and with all the trimmed reads as entry data
from which the contigs smaller than 500 bp were removed after as-
sembly with SPAdes v. 4.0.0. (See Table S2).

2.8 | Benchmarking

The efficiency of the SINCERE-DATA workflow was compared with
manual decontamination on the dataset containing the pure strain
collection. Manual decontamination was performed by predicting
and removing taxa associated with likely contaminants via taxo-
nomic affiliation of reads using Kraken 2 (V. 2.1.3; Wood et al., 2019)
just like in the decontamination methods reported in the literature
(Cornet & Baurain, 2022; Dotan et al., 2023). Enterobacteriaceae
were suspected to be carried by the MDA reagents as well as the
*Salasvirus* from which the Phi29 polymerase is extracted. We also
suspected cross-contamination with *Bacillus* genomic DNA used as
positive controls in some wells; the reads and contigs of Bacilliaceae
were therefore removed from all the SAGs. Sequence reads were
manually decontaminated based on Kraken taxonomy, and reads
affiliated with Eukaryotes, Enterobacteriaceae, *Salasvirus* and
Bacilliaceae were removed. Contamination was evaluated based
on the PlusPFP precompiled database (downloaded on 5 April
2024). These decontamination outputs were subsequently used
to compare the efficiency of workflow decontamination explained
hereafter.

To evaluate the efficiency of SINCERE-DATA decontamination,
the assemblies made from the trimmed reads from the same selected
samples in the published environmental datasets were subjected
to two other automatic decontamination workflows: MDMcleaner
(v. 0.8.7; Vollmers et al., 2022) and MAGpurify (v. 2.1.2; Nayfach
et al., 2019).
<figure><img src="image_8.png" /><figcaption>FIGURE 3 Sequence composition of the gut (n=200), ocean (n=100) and rhizosphere (n=200) datasets with no decontamination of trimmed reads (i.e. only contigs below 500bp were removed) (pink) and with automatic decontamination by SINGLE-CELL REads Decontamination through Automatic Taxonomic Assignation (orange), MAGpurify (yellow), MAGpurify without the coverage option (green) and MDMcleaner (blue). (A) The lengths of assemblages in Kb; (B) the proportion of the assembly removed by the decontamination tools compared with the assembly made with trimmed reads; (C) the proportion of contigs assigned to the dominant taxonomy of the assembly; and (D) the taxonomic classification and relative abundance of the putative contaminant contigs identified with Kraken by each decontamination tool in the published datasets, at the Kingdom and bacterial Phylum levels. The effects of the decontamination methods on other metrics (N50, Contamination and Completeness estimated by CheckM) are provided in Figure S2. Different letters on the plots (a–d) highlight the significant difference of the mean value of the variable between the samples determined with a Dunn test following the Kruskal-Wallis statistical test. Created in https://BioRender.com.</figcaption></figure>
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<figure><img src="image_10.png" /><figcaption>FIGURE 3 (Continued)</figcaption></figure>

MDMcleaner extracts multiple marker genes from an assembled genome and aligns them against a curated reference database. The BLAST hits are used to construct a preliminary lowest common ancestor (LCA) approach. All the genes detected in the assemblies undergo the same LCA classification, and the main taxon of the entire assembly is derived from the main taxon in each contig. A 'trustworthiness' score is then assigned to each contig to exclude contigs likely to be contaminated as well as contigs that require manual evaluation. The workflow was run using default parameters.

MAGPurify is designed to investigate the presence of contaminated contigs using the following options: (1) phylogenetic marker genes, (2) clade-specific marker genes, (3) known contaminants, (4) outlier tetranucleotide frequencies, (5) outlier GC-content and (6) outlier coverage profile. As the coverage option resulted in a drastic drop in the genome length (Figure 3A), we also ran MAGPurify with all the above options except the coverage-based decontamination (i.e. Options 1–5). All the contigs flagged as contaminants by the combination of those approaches were removed from the SAGs.

The decontaminated assemblies provided by SINCERE-DATA, MDMcleaner and MAGpurify were compared with evaluate the quality and purity of the recovered genomes. Assembly quality was assessed using CheckM, and each contig was assigned a taxonomy using Kraken 2.

## 2.9 | Statistical analyses

Prior to statistical test applications, the normality of the residuals distribution of the models was verified with both ggqqplot (ggpubr package, version 0.6.0) and a Shapiro test (Shapiro & Francia, 1972). The homogeneity of variance was evaluated with a

Levene test (O'Neill & Mathews, 2002). On non-parametric data, the Kruskal-Wallis test was used or the Wilcoxon test for paired data (Hervé, 2016), followed by a Dunn test if the statistical result was significant to compare the modalities of the tested variables by pairs.

# 3 | RESULTS

## 3.1 | Decontamination benchmarking

The SINCERE-DATA decontamination procedure differs from the others by (i) conserving the unassigned contigs in the assemblies, (ii) proportionally deleting more contigs with Eukaryota identification in all three datasets and (iii) not removing contigs affiliated with the targeted cells (e.g. Cyanobacteria in the ocean dataset; see Table S5) (Figure 3D). MAGpurify with all options diminishes the assembly length the most in the ocean SAGs, corresponding on average to 57% for the ocean dataset (Figure 3B), which did not improve the proportion of contigs assigned to the target within this dataset (Figure 3C). Indeed, MAGpurify removes a large portion of false contaminants based on kraken2 predictions (56.8% for the ocean dataset and 50.6% for the gut microbiome dataset; Tables S5 and S6), corresponding to contigs that are assigned to the main taxon of the SAG. While no significant differences in the proportion of the contigs of the assembly assigned to the main taxonomy of the SAGs or the N50 (Figure S2) were observed between the decontamination methods in the gut and ocean datasets, SINCERE-DATA was statistically significantly more efficient for the rhizosphere dataset (Figure 3C) and made the biggest cut in the assembly and the most important drop in the N50 measurement (from 6.0 to 3.1 Kb on average) (Figure 3B; Table S7). However, SINCERE-DATA removed, in
all three datasets, various contaminants left out by the other tools (Figure 3D).

## 3.2 | Reliability of quality assessment tools

The putative completeness measurements were correlated with the length of the assembly, where the biggest assemblies had the highest putative completeness measurements (Figure 4a). No linkage was observed between the putative completeness measured after

decontamination with the SINCERE-DATA workflow and the portion of the initial assembly removed by this workflow (Figure 4b). Moreover, no linkage was found between the proportion of assemblies removed using SINCERE-DATA and the putative contamination measured by CheckM (Figure 4c). In the three datasets, there was a systematic decrease in the putative assembly completeness measured by CheckM after the decontamination with SINCERE-DATA (Figure 4d). This decrease implied for some samples a modification of their genome quality category (Bowers, Doud, & Woyke, 2017; Bowers, Kyrpides, et al., 2017), which was, for most of them, deleterious.

<figure><img src="image_11.png" /></figure>

**FIGURE 4** Comparison of the putative completeness and contamination (measured by CheckM) with the SINGle-CEll REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) decontamination for each single-amplified genome (SAG) from the environmental datasets (gut, *n* = 200; ocean, *n* = 100; rhizosphere, *n* = 200). (a) Correlation between the putative genome completeness measured on the published assembly without decontamination and its length in Kb. The regression line is indicated in black. (b) The linkage between the putative completeness and the proportion of the assembly that was removed after automatic decontamination of the raw dataset with SINCERE-DATA. (c) The linkage between the measured putative contamination (CheckM) of the published assembly and the proportion of this assembly that was removed based on contamination identified by SINCERE-DATA. (d) The putative completeness measured by CheckM of the published assemblies with and without decontamination by SINCERE-DATA for each dataset, statistically tested with a Wilcoxon test. The colours of the dots correspond to the assembly quality evaluation of each SAG based on the Genomic Standard Consortium (Bowers, Kyrpides, et al., 2017). Created in https://BioRender.com.
## 3.3 | Decontamination of single-cell-generated bacterial genomic data

Comparison of sequence quality between the NEB and QIAGEN genome amplification and library preparation using positive and negative controls revealed no notable differences, identifying them as possible alternatives (data not shown).

For the manual decontamination, Eukaryotes, Enterobacteriaceae, Salasvirus reads and contigs were removed from the datasets, based on the main taxon detected in the negative controls (Figure S3). SINCERE-DATA also detected Enterobacterales as a contaminant in the lab strains dataset after the first steps of the procedure because of their unexpected taxonomic prevalence in SAG datasets (i.e. they were found to be dominant in more than 70% of the sequence datasets interpreted as being the origin of DNA contamination caused by one of the enzymes we used) and were therefore added to the blacklist. Bacillaceae present in hypothesised 'splash-ome' type contamination were identified as a major contaminant and added to the blacklist by SINCERE-DATA Splashome-finder utility (see methods). In addition to Enterobacterales and Bacillaceae, a total of 16 potential contaminants were identified in this dataset by the SINCERE-DATA workflow in the outlier regions, but only Hominidae and Salasvirus were confirmed as contaminants as they were present in more than 20% of the 'outlier' regions and were the main taxon in less than 20% of the lab strains SAG (Table S4). This threshold was stricter for the environmental dataset where the contaminant status of outlier region taxa was validated if their taxonomy was identical to less than 0.1% of the SAG collection based on their major taxonomy (whereas the threshold was set at 20% for the lab strain dataset which contained more sequences of contaminant origin). A total of 181, 274 and 529 potential contaminants were detected within the ocean, gut and rhizosphere datasets, respectively (Table S4) based on the detection of coverage abnormality within the assemblies. After filtration with the chosen outlier and sample thresholds, 11, 9 and 64 taxa were considered to be contaminants in the ocean gut and rhizosphere datasets, among which some reads from bacteria, archaea, viruses and eukaryotes (Figure 3D; Table S4).

Out of the 30 SAGs selected based on assembly quality (i.e. assembly length >35 Kb) from the lab strain dataset and cleaned, 23 were assigned to S. epidermidis and seven to P. fluorescens, in agreement with what we expected to recover from cell sorting. The SINCERE-DATA decontamination led to an overall conserved length across datasets except for the rhizosphere (Figure 5A). The proportion of contigs taxonomically identical to the isolated cells increased or at least stayed stable after the decontamination procedures in all datasets except for the rhizosphere (Figure 5B). However, some SAGs in the environmental datasets still had only small numbers of correctly assigned contigs after automatic decontamination. These assemblies were composed of genetic material largely unreferenced in databases originating from undescribed or underrepresented bacteria (e.g. SAR86 and SAR116 clades in the ocean dataset; Table S3), with dominance of 'unassigned' contigs in these SAGs. SINCERE-DATA decontamination of published environmental

SAGs ('Published_auto' Figure 5) led to the elimination of potentially chimeric and contaminated contigs, mostly associated with eukaryotes (Figure 6b), in addition to the small contigs also removed in the published datasets (Table S2), which diminishes the length of the decontaminated assemblies. Overall, compared with the published assembly, the SAGs produced from the trimmed reads and cleaned with SINCERE-DATA were longer on average (e.g. Ocean dataset: 960 Kb in the original assembly to 1003 Kb for the cleaned assembly produced by SINCERE-DATA; Gut dataset: 2642–3003 Kb) and more complete according to checkM estimations (Ocean dataset 55.77%–59%, Gut dataset: 65.1%–72.3%). The impact of the SINCERE-DATA decontamination on the SAGs N50 was very dependent on the dataset considered: (Ocean dataset: 63.5–59 Kb, Gut dataset: 14.55–14.85 Kb). The proportion of published assemblies removed by the SINCERE-DATA workflow was highly variable across SAGs, ranging from 0% to 95% of the published genome length (Figure 6a). The contaminant contigs assigned by Kraken 2 were mainly identified as eukaryotes or as several bacterial taxa (Figure 6b).

## 4 | DISCUSSION

The efficiency of the library preparation protocol was evaluated on pure bacterial strains (P. fluorescens and S. epidermidis), and we demonstrated the feasibility of manual tagging of a large number of cells using the barcoded adapters we designed. Plate-based single-cell genomic library preparation kits available on the market allow multiplexing up to 384 samples for a single experiment. Here, we propose an additional layer of multiplexing with only 20 barcodes compatible with commercialised ligase enzyme (QIAseq FX DNA Library Kit QIAGEN), which allows us to individually tag up to 36,864 cells in a single experiment. This solution is cheap, does not require additional sample handling time and can be easily implemented in already existing sample preparation procedures. Our protocol allows the identification of each cell depending on its position on the plate and can be supplemented by the image of the isolated cell acquired with cellenONE to double-check the presence of a single cell in the well. This prevents conservation of genetic information extracted either from empty wells or from wells containing more than one isolated cell. A big advantage of using SCG is the possibility to distinguish between targeted cell DNA and contaminant DNA depending on the sequence profiles (i.e. coverage, fragment sizes, GC content and sequences affiliations) and their prevalence across the SAGs, limiting the risk of producing chimeric genomes. However, this advantage is offset by the whole genome amplification step in which most amplification errors occur, and where short contaminant DNA fragments can be amplified, resulting in low-coverage genome recovery (Lasken & Stockwell, 2007). Particular attention must therefore be paid to this amplification step when envisaging future improvements (Gonzalez-Pena et al., 2021; Stepanauskas et al., 2017). Further miniaturisation of the amplification reaction volumes could reduce MDA biases as well as the number of contaminations (Sobol, 2023).
<figure><img src="image_12.png" /></figure>

FIGURE 5 Metrics of the selected single-amplified genomes (SAGs) with only trimmed reads ('Trimmed', i.e. contigs smaller than 500 bp removed), manual decontamination ('Manual', i.e. Eukaryota, Enterobacteriaceae and Salsavirus removed), automatic decontamination with SINGle-CEII REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) ('Automatic'), the published datasets assemblies ('Published') (see Table S2 for the datasets cleaning processes) and the published assemblies decontaminated with the SINCERE-DATA workflow ('Published_auto'). The median is indicated for each boxplot: (A) Assembly length in Kb and (B) percentage of contigs taxonomically identical to the isolated cell, identified by Kraken 2. The rhizosphere, gut, ocean and lab strain datasets contained 200, 200, 100 and 30 SAGs, respectively. Different letters on the plots (a-c) highlight the significant difference of the mean value of the variable between the samples determined with a Dunn test following the Kruskal-Wallis statistical test.

Despite being chosen for microbial SAGs production, MDMcleaner and MAGpurify revealed (i) their limited capacity to prevent the risk of producing chimeric molecules, thus jeopardising subsequent SAG analyses, in particular, Single Nucleotide Variant (SNV) detection (Goig et al., 2020), but also (ii) their risk of deleting sequences related to the targeted DNA of the sample (Figure 3D). We highlighted herein that using the difference in coverage alone is not sufficient: An additional screening of those regions with abnormal coverage by taxonomic classification is necessary to discriminate contaminants from important coverage differences generated by MDA (Gollnisch et al., 2023). This is, to our knowledge, the first implemented tool for automatic decontamination based on reads rather than contigs (as performed by MDMcleaner; Vollmers et al., 2022 or MAGpurify; Nayfach et al., 2019). Overall, SINCERE-DATA allows an adaptive decontamination of datasets depending on the contamination load with more flexibility than MAGpurify and MDMcleaner, which will not discriminate the sequences of interest and contaminants at scales as precise as SINCERE-DATA by missing contaminants lacking genomic markers.

With the SINCERE-DATA workflow, we were able to identify the presence of contaminants spanning multiple samples in a given dataset and to clean up SAGs with a degree of precision that approached that of manual decontamination. On pure strains, the workflow detected the same contaminants as those identified manually, and their automatic removal led to similar final assembly quality metrics as manual decontamination procedures (Figure 5). Moreover, no sequence of the targeted strains (P. fluorescens and S. epidermidis) was removed automatically (Table S4). This demonstrates that SINCERE-DATA is able to selectively remove the contamination even in a heavily contaminated dataset. In samples in the environmental datasets, we still detected potential contaminant sequences after the removal of contigs shorter than 200, 1000 or 2000 bp which represented the only cleaning procedure of the published assemblies (Aoki et al., 2022; Berube et al., 2018; Kawano-Sugaya et al., 2024) (Table S2; Figure 6). These sequences were removed by the SINCERE-DATA workflow at both the read and contig levels, hence improving the purity of the SAGs (Figure 5B) while keeping non contaminant short contigs that bear relevant genomic
<figure><img src="image_13.png" /><figcaption>FIGURE 6 Decontamination of the reads conserved in the environmental single-amplified genomes using the SINgle-CEll REads Decontamination through Automatic Taxonomic Assignation (SINCERE-DATA) workflow. (a) Proportion of published assemblies removed after decontamination with SINCERE-DATA for each dataset (ocean n = 100; gut n = 200; rhizosphere n = 200). (b) Relative abundance of the taxonomy of the contigs identified by Kraken 2 and removed, at the Kingdom level and Phylum and Class (bacteria only) levels.</figcaption></figure>

information. Regardless of the decontamination procedure used,
we noticed great differences in the assembly length and purity be-
tween the datasets (Figures 3 and 5) which limits their comparison.
Indeed, the source of the sample, the sample preparation strategy,
and the sample trimming vary between the studies and impacts the
quality of the datasets. The variability of the putative contamina-
tion profiles between SAGs from different environmental dataset
(Figure 6), additionally confirms the need for a SAG-specific decon-
tamination step that is made possible by the SINCERE-DATA work-
flow, in which, beyond the benefits of automation, the settings are
customisable.

Environmental bacterial genome reconstruction procedures are
confronted with a consequent proportion of unassigned sequences
present in the sequencing dataset, but also with approximations of
bacterial sequence identifications in databases (Wright et al., 2023).
SINCERE-DATA is also inevitably constrained by these databases.

An interesting perspective to overcome this limitation could be
to explore the use of kmer diversity counting, although the use of
kmer counting for microbial diversity analysis is still currently exper-
imental (Bokulich, 2025; Figueroa III et al., 2024). The combination
of a first screening of now-known and predicted contaminants in
the reads prior to the assembly and again in the contigs allows for
more refined decontamination by diminishing the risk of generating
chimeric contigs and predicting false SNVs while conserving the yet
non-identified reads that would participate in the assembly as addi-
tional bacterial genomic information. Likewise, because identifying
closely related bacterial genomes is greatly limited for environmen-
tal data, SINCERE-DATA will not discriminate further than the genus
level as discriminating reads from closely related genes would prove
challenging and unreliable. To avoid losing mobile genetic elements,
SINCERE-DATA incorporates a functionality to seek plasmids in the
filtered contigs to reinject them into the cleaned assembly. In an
<figure><img src="image_14.png" /></figure>

upcoming SINCERE-DATA version, prophage detection will also be implemented.

Low-quality assemblies and chimaeras in SAGs are usually detected by measuring putative contamination and completeness using bioinformatic tools (Bowers, Kyrpides, et al., 2017; Cornet & Baurain, 2022). A variety of tools exist for the evaluation of genome completeness and contamination including, for instance, Anvi'o (Eren et al., 2015), ProDeGe (Tennessen et al., 2015) and acdc (Lux et al., 2016), but CheckM is by far the most commonly used to evaluate SAG quality (Anstett et al., 2023; Chijiiwa et al., 2020; Nishikawa et al., 2022). Our results emphasise the problem of the reliability of these genome quality estimates, notably with CheckM. If this approach were accurate, we would have expected to detect more contaminant sequences with the SINCERE-DATA workflow in low-quality assemblies estimated by CheckM (Figure 4b). Instead, some SAGs classified as 'high' and 'medium' quality by CheckM before the SINCERE-DATA decontamination were moved to the 'low' quality category after decontamination, which led to the removal of a high proportion of sequences (Figure 4b,d), resulting in a marked decrease in measured putative completeness. For most of these SAGs, the taxonomic classification before the decontamination performed by Kraken 2 included a variety of genomic elements from different prokaryote classes. This drop in putative completeness was not observed in well-classified genomes for which the SINCERE-DATA decontamination procedure did not result in a significant loss of putative completeness. The CheckM tool detects broad sets of genes across bacterial and archaeal genomes and is therefore necessarily influenced by the assembly length (Figure 4a) and the content of the reference data used (Parks et al., 2015). In fact, CheckM can provide very good metrics and also wrong putative metrics in two identified cases: (1) when the genome is from a lineage lacking high-quality genomic representation (Chklovski et al., 2023) and (2) when contamination is redundant or complementary to the analysed genome given the fact that CheckM uses the multiplicity of supposedly single copied marker genes to produce the estimates (Vollmers et al., 2022). Finally, CheckM is unable to detect Eukaryote contamination reliably (Cornet & Baurain, 2022). To increase the purity of the SAGs, we recommend supplementing the CheckM estimation of contamination with a contamination prediction relying on genome-wide k-mer-based taxonomic classification of contigs, such as Kraken 2, which provides multiple well-curated inter-domain databases to assess the presence of potential contaminant and chimeric contigs in the assembly.

## 5 | CONCLUSION

Complemented by each other, the combination of wet-lab strategy and in silico data treatment allowed us to identify, circumvent and improve library preparation (e.g. know-how; multiplexing) and sequence data workflow (e.g. identification of contaminant DNA sources). We foresee future improvements to this unifying workflow to analyse and detect population variants within bacterial

communities and predict the use of high-throughput SCG as a possible replacement for metagenomics. Detecting environmental DNA with SCG decontamination tools, such as SINCERE-DATA, could also improve the recovery of metagenomes from environmental samples that are contaminated and increase their purity.

## AUTHORS' CONTRIBUTION

Philippe Vandenkoornhuyse, Cecile Monard and Cécile Thion have supervised the project. They all corrected the manuscript. With the help of Cécile Thion, Solène Mauger designed and ran the experiments to create the library preparation workflow. Léna Carret was then able to apply this protocol by following the instructions of the previously developed protocol to produce the sequencing data. Yann Sevellec has been the main developer of the bioinformatic pipeline and wrote the code, with the help of Noé Robert, who was a student intern at the time, and Laura Jarassier, who was a support for the bioinformatic brainstorming. Solène Mauger wrote most of the manuscript, produced most of the figures and tables, and interpreted all the results. Yann Sevellec wrote most of the bioinformatic methods and was a support for bioinformatic interpretation, troubleshooting and discussion.

## ACKNOWLEDGEMENTS

This work was supported by the LabCom 'Microscale Lab' grant from ANR ('Agence Nationale de la Recherche', 21-LCV1-0003-01), an industrial doctoral CIFRE project between Cellenion company based in Lyon, France and the University of Rennes and by the MICROBES4-CLIMATE project which has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101131818. However, this document reflects only the authors' view and the European Commission is not responsible for any use that may be made of the information it contains. We are most grateful to Biogenouest, ANAEE-France and the EcogenO core facility of Rennes for the technical support and to Daphne Goodfellow for the English editing.

## CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest to declare.

## PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210X.70170.

## DATA AVAILABILITY STATEMENT

The published dataset used in this study is available at https://doi.org/10.6084/m9 figshare.c.7834604.v2 (Mauger et al., 2025), and actively maintained version of SINCERE-DATA is open source and available at GitHub: https://github.com/ysevel/SINCERE_DATA (Mauger et al., 2025). A user friendly interface of SINCERE-DATA is also available in Lifewatch ERIC upon request at https://my.lifewatch.
eu/workflow/create-workflow-from-template/sincere-data. A zenodo
archive at https://doi.org/10.5281/zenodo.17104423 is also available
(Sevellec, 2025). SINCERE-DATA: SINGle-CEll REads Decontamination
through Automatic Taxonomic Assignation (V.1.1.0).

STATEMENT ON INCLUSION

Our study was conducted both in Rennes and Lyon with permanent
feedback between the two institutions. The tasks were shared
within research groups from the University of Rennes and Cellenion
to cover various expertise required by the study (bioinformatics,
microbiology, molecular biology).

ORCID

Solène Mauger https://orcid.org/0000-0002-1322-2003

Philippe Vandenkoornhuyse https://orcid.
org/0000-0003-3029-4647

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Table S1.** Adaptators list for combinatorial barcoding.

**Table S2.** Overview of contaminant sequences after the removal of contigs shorter than 200, 1000 or 2000bp from the published datasets assemblies.

**Table S3.** Metadata of the chosen SAGs.

**Table S4.** Putative contaminating DNA detected within the ocean, gut and rhizosphere datasets.

**Table S5.** Overview of the different metrics related to the published Ocean datasets assemblies decontamination using the different tools assessed within this study.

**Table S6.** Overview of the different metrics related to the published gut dataset assemblies decontamination using the different tools assessed within this study.

**Table S7.** Overview of the different metrics related to the published rhizosphere dataset assemblies decontamination using the different tools assessed within this study.

**Figure S1.** Main steps in the preparation of samples for single-cell library sequencing.

**Figure S2.** Sequence composition of the gut (n = 200), ocean (n = 100) and rhizosphere (n = 200) datasets with no decontamination of trimmed reads (i.e. only contigs below 500bp were removed) (pink) and with automatic decontamination by SINCERE-DATA (orange), MAGpurify (yellow), MAGpurify without the coverage option (green) and MDMcleaner (blue).

**Figure S3.** Taxonomy at the family level of the reads found in the 22 negative controls of the lab strains experiment.

**How to cite this article:** Mauger, S., Sevellec, Y., Carret, L., Robert, N., Monard, C., Thion, C., Jarassier, L., & Vandenkoornhuyse, P. (2025). Wet-lab preparation and automated dataset decontamination procedure for bacterial single-cell genomics. *Methods in Ecology and Evolution, 00*, 1–17. https://doi.org/10.1111/2041-210X.70170