Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells.

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.

Increased intake of tree forage by moose is associated with intake of crops rich in nonstructural carbohydrates.

Animals representing a wide range of taxonomic groups are known to select specific food combinations to achieve a nutritionally balanced diet. The nutrient balancing hypothesis suggests that, when given the opportunity, animals select foods to achieve a particular target nutrient balance, and that balancing occurs between meals and between days. For wild ruminants who inhabit landscapes dominated by human land use, nutritionally imbalanced diets can result from ingesting agricultural crops rich in starch and sugar (nonstructural carbohydrates [NCs]), which can be provided to them by people as supplementary feeds. Here, we test the nutrient balancing hypothesis by assessing potential effects that the ingestion of such crops by Alces alces (moose) may have on forage intake. We predicted that moose compensate for an imbalanced intake of excess NC by selecting tree forage with macro-nutritional content better suited for their rumen microbiome during wintertime. We applied DNA metabarcoding to identify plants in fecal and rumen content from the same moose during winter in Sweden. We found that the concentration of NC-rich crops in feces predicted the presence of Picea abies (Norway spruce) in rumen samples. The finding is consistent with the prediction that moose use tree forage as a nutritionally complementary resource to balance their intake of NC-rich foods, and that they ingested P. abies in particular (normally a forage rarely eaten by moose) because it was the most readily available tree. Our finding sheds new light on the foraging behavior of a model species in herbivore ecology, and on how habitat alterations by humans may change the behavior of wildlife.

sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility.

Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.

sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing.

Here we present advancements in single-cell combinatorial indexed Assay for Transposase Accessible Chromatin (sciATAC) to measure chromatin accessibility that leverage nanowell chips to achieve atlas-scale cell throughput (>105 cells) at low cost. The platform leverages the core of the sciATAC workflow where multiple indexed tagmentation reactions are performed, followed by pooling and distribution to a second set of reaction wells for polymerase chain reaction (PCR)-based indexing. In this work, we instead leverage a chip containing 5184 nanowells at the PCR stage of indexing, enabling a 52-fold improvement in scale and reduction in per-cell preparation costs. We detail three variants that balance cell throughput and depth of coverage, and apply these methods to banked mouse brain tissue, producing maps of cell types as well as neuronal subtypes that include integration with existing single-cell Assay for Transposase Accessible Chromatin (scATAC) and scRNA-seq data sets. Our optimized workflow achieves a high fraction of reads that fall within called peaks (>80%) and low cell doublet rates. The high cell coverage technique produces high unique reads per cell, while retaining high enrichment for open chromatin regions, enabling the assessment of >70,000 unique accessible loci on average for each cell profiled. When compared to current methods in the field, our technique provides similar or superior per-cell information with very low levels of cell-to-cell cross talk, and achieves this at a cost point much lower than existing assays.

High-throughput robust single-cell DNA methylation profiling with sciMETv2.

DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. We previously described a proof-of-principle technique that enabled high cell throughput; however, it produced only low-coverage profiles and was a difficult protocol that required custom sequencing primers and recipes and frequently produced libraries with excessive adapter contamination. Here, we describe a greatly improved version that generates high-coverage profiles (~15-fold increase) using a robust protocol that does not require custom sequencing capabilities, includes multiple stopping points, and exhibits minimal adapter contamination. We demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. Finally, we demonstrate the ability to determine cell types using CG methylation alone, which is the dominant context for DNA methylation in most cell types other than neurons and the most applicable analysis outside of brain tissue.

Targeted Amplification and Sequencing of Ancient Environmental and Sedimentary DNA.

All organisms release their DNA into the environment through processes such as excretion and the senescence of tissues and limbs. This DNA, often referred to as environmental DNA (eDNA) or sedimentary ancient DNA (sedaDNA), can be recovered from both present-day and ancient soils, fecal samples, bodies of water and lake cores, and even air. While eDNA is a potentially useful record of past and present biodiversity, several challenges complicate data generation and interpretation of results. Most importantly, eDNA samples tend to be highly taxonomically mixed, and the target organism or group of organisms may be present at very low abundance within this mixture. To overcome this challenge, enrichment approaches are often used to target specific taxa of interest. Here, we describe a protocol to amplify metabarcodes or short, variable loci that identify lineages within broad taxonomic groups (e.g., plants, mammals), using the polymerase chain reaction (PCR) with established generic "barcode" primers. We also provide a catalog of animal and plant barcode primers that, because they target relatively short fragments of DNA, are potentially suitable for use with degraded DNA.

Minimizing polymerase biases in metabarcoding.

DNA metabarcoding is an increasingly popular method to characterize and quantify biodiversity in environmental samples. Metabarcoding approaches simultaneously amplify a short, variable genomic region, or "barcode," from a broad taxonomic group via the polymerase chain reaction (PCR), using universal primers that anneal to flanking conserved regions. Results of these experiments are reported as occurrence data, which provide a list of taxa amplified from the sample, or relative abundance data, which measure the relative contribution of each taxon to the overall composition of amplified product. The accuracy of both occurrence and relative abundance estimates can be affected by a variety of biological and technical biases. For example, taxa with larger biomass may be better represented in environmental samples than those with smaller biomass. Here, we explore how polymerase choice, a potential source of technical bias, might influence results in metabarcoding experiments. We compared potential biases of six commercially available polymerases using a combination of mixtures of amplifiable synthetic sequences and real sedimentary DNA extracts. We find that polymerase choice can affect both occurrence and relative abundance estimates and that the main source of this bias appears to be polymerase preference for sequences with specific GC contents. We further recommend an experimental approach for metabarcoding based on results of our synthetic experiments.

A probable prehistoric case of meningococcal disease from San Francisco Bay: Next generation sequencing of Neisseria meningitidis from dental calculus and osteological evidence.

Next Generation Sequencing (NGS) of ancient dental calculus samples from a prehistoric site in San Francisco Bay, CA-SCL-919, reveals a wide range of potentially pathogenic bacteria. One older adult woman, in particular, had high levels of Neisseria meningitidis and low levels of Haemophilus influenzae, species that were not observed in the calculus from three other individuals. Combined with the presence of incipient endocranial lesions and pronounced meningeal grooves, we interpret this as an ancient case of meningococcal disease. This disease afflicts millions around the globe today, but little is known about its (pre)history. With additional sampling, we suggest NGS of calculus offers an exciting new window into the evolutionary history of these bacterial species and their interactions with humans.

Diet Assessment Based on Rumen Contents: A Comparison between DNA Metabarcoding and Macroscopy.

Dietary choices are central to our understanding of ecology and evolution. Still, many aspects of food choice have been hampered by time consuming procedures and methodological problems. Faster and cheaper methods, such as DNA metabarcoding, have therefore been widely adopted. However, there is still very little empirical support that this new method is better and more accurate compared to the classic methods. Here, we compare DNA metabarcoding to macroscopic identifications of rumen contents in two species of wild free-ranging ungulates: roe deer and fallow deer. We found that the methods were comparable, but they did not completely overlap. Sometimes the DNA method failed to identify food items that were found macroscopically, and the opposite was also true. However, the total number of taxa identified increased using DNA compared to the macroscopic analysis. Moreover, the taxonomic precision of metabarcoding was substantially higher, with on average 90% of DNA-sequences being identified to genus or species level compared to 75% of plant fragments using macroscopy. In niche overlap analyses, presence/absence data showed that both methods came to very similar conclusions. When using the sequence count data and macroscopic weight, niche overlap was lower than when using presence-absence data yet tended to increase when using DNA compared to macroscopy. Nevertheless, the significant positive correlation between macroscopic quantity and number of DNA sequences counted from the same plant group give support for the use of metabarcoding to quantify plants in the rumen. This study thus shows that there is much to be gained by using metabarcoding to quantitatively assess diet composition compared to macroscopic analysis, including higher taxonomic precision, sensitivity and cost efficiency.

Using eDNA to experimentally test ungulate browsing preferences.

Large herbivores may affect ecosystem processes and states, but such effects can be difficult to quantify, especially within multispecies assemblages. To better understand such processes and improve our predictive ability of systems undergoing change, herbivore diets can be studied using controlled feeding trials (or cafeteria tests). With some wildlife, such as large herbivores, it is impractical to empirically verify these findings, because it requires visually observing animals in forested environments, which can disturb them from their natural behaviors. Yet, in field-based cafeteria trials it is nearly impossible to differentiate selection between herbivore species that forage on similar plants and make very similar bite marks. However, during browsing ungulates leave saliva residue which includes some buccal cells and DNA that can be extracted for species identification. Here we used a newly developed eDNA-based method (biteDNA) to test the browsing preferences of four sympatric ungulate species in the wild. Overall, food preferences varied between species, but all species strongly preferred deciduous over coniferous species. Our method allows the study of plant-animal interactions in multispecies assemblages at very fine detail.

DNA left on browsed twigs uncovers bite-scale resource use patterns in European ungulates.

Fine-scale resource use by large herbivores is often difficult to quantify directly. This is particularly true for browsing ungulates due to the challenges in observing shy subjects in forested environments of low visibility. As a consequence we know relatively little about resource use by diverse browsing ungulates. When browsing, ungulates leave behind saliva on the browsed twig that includes their DNA, which can be used to identify the species that was responsible for browsing the twig. We used this method, which we term "biteDNA", to study bite-scale browsing patterns in a temperate ungulate community. This approach provides a level of detail in browsing patterns across species that was previously very hard to attain. We found that all deer species largely overlapped in terms of the tree species they used. Moose browsed larger diameters than red deer and roe deer, but these latter two species did not differ. Moose browsed at higher heights than red deer, and red deer higher than roe deer. Although the deer species differed in mean browsing height, species were comparable in terms of their minimum browsing height of ~20 cm. This means that height and diameter ranges of the smaller species were found to be completely inside the ranges of the larger species. Hence, while moose may access exclusive food resources in terms of browse height and diameter, red and roe deer cannot.

Browsed twig environmental DNA: diagnostic PCR to identify ungulate species.

Ungulate browsing can have a strong effect on ecological processes by affecting plant community structure and composition, with cascading effects on nutrient cycling and animal communities. However, in the absence of direct observations of foraging, species-specific foraging behaviours are difficult to quantify. We therefore know relatively little about foraging competition and species-specific browsing patterns in systems with several browsers. However, during browsing, a small amount of saliva containing buccal cells is deposited at the bite site, providing a source of environmental DNA (eDNA) that can be used for species identification. Here, we describe extraction and PCR protocols for a browser species diagnostic kit. Species-specific primers for mitochondrial DNA were optimized and validated using twigs browsed by captive animals. A time series showed that about 50% of the samples will amplify up to 12 weeks after the browsing event and that some samples amplify up to 24 weeks after browsing (12.5%). Applied to samples of natural browsing from an area where moose (Alces alces), roe deer (Capreolus capreolus), fallow deer (Cervus dama) and red deer (Cervus elaphus) are sympatric, amplification success reached 75%. This method promises to greatly improve our understanding of multispecies browsing systems without the need for direct observations.