Systematic benchmarking of single-cell ATAC-sequencing protocols

Data de publicació electrònica: 03-08-2023Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.H.H. received support for the project PID2020-115439GB-I00 funded by MCIN/AEI/10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement numbers 810287 and 874710). This work was supported by a European Research Council Consolidator grant to S.A. (724226_cis-CONTROL), KU Leuven (C14/22/125 to S.A.), Foundation Against Cancer (F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A. and a PhD fellowship to F.D.) and Aligning Science Across Parkinson’s (grant number ASAP-000430 to S.A.). K.B.M. and S.A.T. are supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194). Computing was performed at the Vlaams Supercomputer Center and high-throughput sequencing at the Genomics Core Leuven. M.R.C. is supported by the National Institutes on Aging K99/R00AG059918. This work was supported by funding from the Rita Allen Foundation (W.J.G.) and the Human Frontiers Science Program (RGY006S; W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative and National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01-HG00990901 and U19-AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective. B.D. received financial support by Swiss National Science Foundation 310030_197082 and the EPFL. L.S.L. receives support from an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1), a National Institutes of Health grant UM1HG012076, a Longevity Impetus grant and a Hector Research Career Development Award by the Hector Fellow Academy. A.C.A. is supported by National Institutes of Health grants RF1-MH128842, R35-GM124704 and R01-DA047237 as well as a Silver Family Foundation Innovator Award

A Systems Map to Elucidate the Factors Influencing Vaccine Coverage

status: Published onlin

Decoding gene regulation in the fly brain

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation

Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads

Single-cell RNA-seq and single-cell assay for transposase-accessible chromatin (ATAC-seq) technologies are used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost and pave the way for more specialized multiome assays, custom droplet microfluidics may provide solutions complementary to commercial setups. We developed HyDrop, a flexible and open-source droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost noncommercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 7996 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA’s high capture rate to analyze a small population of fluorescence-activated cell sorted neurons from the Drosophila brain, confirming the protocol’s applicability to low input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi) omics protocols

Creasensor: SIMPLE technology for creatinine detection in plasma

The lab-on-a-chip (LOC) field has witnessed an excess of new technology concepts, especially for the point-of-care (POC) applications. However, only few concepts reached the POC market often because of challenging integration with pumping and detection systems as well as with complex biological assays. Recently, a new technology termed SIMPLE was introduced as a promising POC platform due to its features of being self-powered, autonomous in liquid manipulations, cost-effective and amenable to mass production. In this paper, we improved the SIMPLE design and fabrication and demonstrated for the first time that the SIMPLE platform can be successfully integrated with biological assays by quantifying creatinine, biomarker for chronic kidney disease, in plasma samples. To validate the robustness of the SIMPLE technology, we integrated a SIMPLE-based microfluidic cartridge with colorimetric read-out system into the benchtop Creasensor. This allowed us to perform on-field validation of the Creasensor in a single-blind study with 16 plasma samples, showing excellent agreement between measured and spiked creatinine concentrations (ICC: 0.97). Moreover, the range of clinically relevant concentrations (0.76–20 mg/dL), the sample volume (5 μL) and time-to-result of only 5 min matched the Creasensor performance with both lab based and POC benchmark technologies. This study demonstrated for the first time outstanding robustness of the SIMPLE in supporting the implementation of biological assays. The SIMPLE flexibility in liquid manipulation and compatibility with different sample matrices opens up numerous opportunities for implementing more complex assays and expanding its POC applications portfolio.status: publishe

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Acknowledgements: H.H. received support for the project PID2020-115439GB-I00 funded by MCIN/AEI/10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement numbers 810287 and 874710). This work was supported by a European Research Council Consolidator grant to S.A. (724226_cis-CONTROL), KU Leuven (C14/22/125 to S.A.), Foundation Against Cancer (F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A. and a PhD fellowship to F.D.) and Aligning Science Across Parkinson’s (grant number ASAP-000430 to S.A.). K.B.M. and S.A.T. are supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194). Computing was performed at the Vlaams Supercomputer Center and high-throughput sequencing at the Genomics Core Leuven. M.R.C. is supported by the National Institutes on Aging K99/R00AG059918. This work was supported by funding from the Rita Allen Foundation (W.J.G.) and the Human Frontiers Science Program (RGY006S; W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative and National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01-HG00990901 and U19-AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective. B.D. received financial support by Swiss National Science Foundation 310030_197082 and the EPFL. L.S.L. receives support from an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1), a National Institutes of Health grant UM1HG012076, a Longevity Impetus grant and a Hector Research Career Development Award by the Hector Fellow Academy. A.C.A. is supported by National Institutes of Health grants RF1-MH128842, R35-GM124704 and R01-DA047237 as well as a Silver Family Foundation Innovator Award.Funder: H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).Funder: M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.Funder: K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.Funder: This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols