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High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Advances in the biological sciences have led to an ongoing paradigm shift in toxicity testing based on expanded application of high-throughput in vitro screening and in silico methods to assess potential health risks of environmental agents. This review examines progress on the vision for toxicity testing elaborated by the US National Research Council (NRC) during the decade that has passed since the 2007 NRC report on Toxicity Testing in the 21st Century (TT21C). Concomitant advances in exposure assessment, including computational approaches and high-throughput exposomics, are also documented. A vision for the next generation of risk science, incorporating risk assessment methodologies suitable for the analysis of new toxicological and exposure data, resulting in human exposure guidelines is described. Case study prototypes indicating how these new approaches to toxicity testing, exposure measurement, and risk assessment are beginning to be applied in practice are presented. Overall, progress on the 20-year transition plan laid out by the US NRC in 2007 has been substantial. Importantly, government agencies within the United States and internationally are beginning to incorporate the new approach methodologies envisaged in the original TT21C vision into regulatory practice. Future perspectives on the continued evolution of toxicity testing to strengthen regulatory risk assessment are provided.

Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis. Transcriptome analysis of circulating tumor cells (CTCs) provides insights into monitoring target therapeutics and underlying tumor metastasis. Here the authors present Hydro-Seq, a contamination-free high-throughput hydrodynamic scRNA-seq barcoding technique for rare CTCs.

High‐throughput sequencing of the 16S rRNA gene on the Illumina platform is commonly used to assess microbial diversity in environmental samples. The MiniSeq, Illumina's latest benchtop sequencer, enables more cost‐efficient DNA sequencing relative to larger Illumina sequencing platforms (e.g., MiSeq). Here we used a modified custom primer sequencing approach to test the fidelity of the MiniSeq for high‐throughput sequencing of the V4 hypervariable region of 16S rRNA genes from complex communities in environmental samples. To this end, we designed additional sequencing primers that enabled application of a dual‐index barcoding method on the MiniSeq. A mock community was sequenced alongside the environmental samples in four different sequencing runs as a quality control benchmark. We were able to recapture a realistic richness of the mock community in all sequencing runs, and identify meaningful differences in alpha and beta diversity in the environmental samples. Furthermore, rarefaction analysis indicated diversity in many environmental samples was close to saturation. These results show that the MiniSeq can produce similar quantities of high‐quality V4 reads compared to the MiSeq, yet is a cost‐effective option for any laboratory interested in performing high‐throughput 16S rRNA gene sequencing. Innovation in next‐generation DNA sequencing technology continues to contribute to the democratization of 16S rRNA gene sequencing, which is now often carried out on the Illumina MiSeq and HiSeq platforms. Here, we describe a 16S rRNA gene sequencing protocol for the latest benchtop high‐throughput sequencer, the Illumina MiniSeq, which provides comparable quantity and quality of data, but at significantly reduced cost compared to larger and more expensive sequencers. This opens the opportunity for smaller labs to perform their own 16S sequencing independently.

- Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS. - To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care. - The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center. - The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies. Synopsis The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations. CPA can be trained on highly multiplexed, single‐cell experiments with thousands of conditions to predict unmeasured phenotypes (e.g., specific dose responses). It can generalize to predict responses to small molecules never seen in the training by adding priors on chemical space. Validations using a newly generated combinatorial drug perturbation dataset demonstrate the accuracy of CPA in predicting unseen drug combinations. CPA is also applicable to genetic combinatorial screens, as shown by imputing in silico 5,329 missing combinations in a single‐cell perturb‐seq experiment with diverse genetic interactions. Graphical Abstract The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.

Recent advances in sequencing technologies now permit the analyses of plant DNA from fossil samples (ancient plant DNA, plant aDNA), and thus enable the molecular reconstruction of palaeofloras.Hitherto, ancient frozen soils have proved excellent in preservingDNAmolecules, and have thus been the most commonly used source of plant aDNA. However, DNA from soil mainly represents taxa growing a fewmetres fromthe sampling point. Lakes have larger catchment areas and recent studies have suggested that plant aDNAfromlake sediments is a more powerful tool for palaeofloristic reconstruction. Furthermore, lakes can be found globally in nearly all environments, and are therefore not limited to perennially frozen areas. Here,we review the latest approaches and methods for the study of plant aDNA from lake sediments and discuss the progressmade up to the present.Weargue that aDNAanalyses add newand additional perspectives for the study of ancient plant populations and, in time, will provide higher taxonomic resolution and more precise estimation of abundance. Despite this, key questions and challenges remain for such plant aDNA studies. Finally, we provide guidelines on technical issues, including lake selection, and we suggest directions for future research on plant aDNA studies in lake sediments.

Mining the antibody repertoire of plasma cells and plasmablasts could enable the discovery of useful antibodies for therapeutic or research purposes 1 . We present a method for high-throughput, single-cell screening of IgG-secreting primary cells to characterize antibody binding to soluble and membrane-bound antigens. Celli GO is a droplet microfluidics system that combines high-throughput screening for IgG activity, using fluorescence-based in-droplet single-cell bioassays 2 , with sequencing of paired antibody V genes, using in-droplet single-cell barcoded reverse transcription. We analyzed IgG repertoire diversity, clonal expansion and somatic hypermutation in cells from mice immunized with a vaccine target, a multifunctional enzyme or a membrane-bound cancer target. Immunization with these antigens yielded 100–1,000 IgG sequences per mouse. We generated 77 recombinant antibodies from the identified sequences and found that 93% recognized the soluble antigen and 14% the membrane antigen. The platform also allowed recovery of ~450–900 IgG sequences from ~2,200 IgG-secreting activated human memory B cells, activated ex vivo, demonstrating its versatility. Millions of primary IgG-secreting cells from mouse and human are characterized for activity and antibody sequence at the single-cell level.

Seq-Well provides similar scale and data quality to massively parallel, droplet-based single-cell RNA-seq methods in an easy to use, inexpensive and portable microwell format compatible with low-input samples. Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis .

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer’s disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer’s disease, Parkinson’s disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases. Yang et al. generated a genomic atlas of protein levels in brain, cerebrospinal fluid and plasma and used human genetics approaches to identify proteins implicated in neurological diseases as well as druggable targets.