Functional characterization of pseudouridine (Ψ) in mammalian mRNA has been hampered by the lack of a quantitative method that maps Ψ in the whole transcriptome. We report bisulfite-induced deletion sequencing (BID-seq), which uses a bisulfite-mediated reaction to convert pseudouridine stoichiometrically into deletion upon reverse transcription without cytosine deamination. BID-seq enables detection of abundant Ψ sites with stoichiometry information in several human cell lines and 12 different mouse tissues using 10–20 ng input RNA. We uncover consensus sequences for Ψ in mammalian mRNA and assign different ‘writer’ proteins to individual Ψ deposition. Our results reveal a transcript stabilization role of Ψ sites installed by TRUB1 in human cancer cells. We also detect the presence of Ψ within stop codons of mammalian mRNA and confirm the role of Ψ in promoting stop codon readthrough in vivo. BID-seq will enable future investigations of the roles of Ψ in diverse biological processes.

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Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI

Liu, Cong; Sun, Hanxiao; Yi, Yunpeng; Shen, Weiguo; Li, Kai; Xiao, Ye; Li, Fei; Li, Yuchen; Hou, Yongkang; Lu, Bo; Liu, Wenqing; Meng, Haowei; Peng, Jinying; Yi, Chengqi; Wang, Jing

2023 Nature Biotechnology

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Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes

Qiao, Chang; Li, Di; Liu, Yong; Zhang, Siwei; Liu, Kan; Liu, Chong; Guo, Yuting; Jiang, Tao; Fang, Chuyu; Li, Nan; Zeng, Yunmin; He, Kangmin; Zhu, Xueliang; Lippincott-Schwartz, Jennifer; Dai, Qionghai; Li, Dong

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Open Access Collection

Precision mitochondrial DNA editing with high-fidelity DddA-derived base editors

Lee, Seonghyun; Lee, Hyunji; Baek, Gayoung; Kim, Jin-Soo

2023 Nature Biotechnology

doi: 10.1038/s41587-022-01486-wpmid: 36229610

Bacterial toxin DddA-derived cytosine base editors (DdCBEs)—composed of split DddAtox (a cytosine deaminase specific to double-stranded DNA), custom-designed TALE (transcription activator-like effector) DNA-binding proteins, and a uracil glycosylase inhibitor—enable mitochondrial DNA (mtDNA) editing in human cells, which may pave the way for therapeutic correction of pathogenic mtDNA mutations in patients. The utility of DdCBEs has been limited by off-target activity, which is probably caused by spontaneous assembly of the split DddAtox deaminase enzyme, independent of DNA-binding interactions. We engineered high-fidelity DddA-derived cytosine base editors (HiFi-DdCBEs) with minimal off-target activity by substituting alanine for amino acid residues at the interface between the split DddAtox halves. The resulting domains cannot form a functional deaminase without binding of their linked TALE proteins at adjacent sites on DNA. Whole mitochondrial genome sequencing shows that, unlike conventional DdCBEs, which induce hundreds of unwanted off-target C-to-T conversions in human mtDNA, HiFi-DdCBEs are highly efficient and precise, avoiding collateral off-target mutations, and as such, they will probably be desirable for therapeutic applications.

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LitStream Collection

Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

Li, Chen; Virgilio, Maria C.; Collins, Kathleen L.; Welch, Joshua D.

2023 Nature Biotechnology

doi: 10.1038/s41587-022-01476-ypmid: 36229609

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub (https://github.com/welch-lab/MultiVelo).

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Open Access Collection

Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Allesøe, Rosa Lundbye; Lundgaard, Agnete Troen; Hernández Medina, Ricardo; Aguayo-Orozco, Alejandro; Johansen, Joachim; Nissen, Jakob Nybo; Brorsson, Caroline; Mazzoni, Gianluca; Niu, Lili; Biel, Jorge Hernansanz; Leal Rodríguez, Cristina; Brasas, Valentas; Webel, Henry; Benros, Michael Eriksen; Pedersen, Anders Gorm; Chmura, Piotr Jaroslaw; Jacobsen, Ulrik Plesner;

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LitStream Collection

Precise DNA cleavage using CRISPR-SpRYgests

Christie, Kathleen A.; Guo, Jimmy A.; Silverstein, Rachel A.; Doll, Roman M.; Mabuchi, Megumu; Stutzman, Hannah E.; Lin, Jiecong; Ma, Linyuan; Walton, Russell T.; Pinello, Luca; Robb, G. Brett; Kleinstiver, Benjamin P.

2023 Nature Biotechnology

doi: 10.1038/s41587-022-01492-y

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LitStream Collection

Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes

Gao, Teng; Soldatov, Ruslan; Sarkar, Hirak; Kurkiewicz, Adam; Biederstedt, Evan; Loh, Po-Ru; Kharchenko, Peter V.

2023 Nature Biotechnology

doi: 10.1038/s41587-022-01468-ypmid: 36163550

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

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LitStream Collection

Longitudinal data support university-based biomedical entrepreneurship education programs

Vizgan, Gabriel; Hill-Whilton, Zachary; Achuonjei, Joy; Schweickart, Tucker; Chitale, Sadhana; Gillespie, Colleen; Gold-von Simson, Gabrielle

2023 Nature Biotechnology

doi: 10.1038/s41587-023-01701-2pmid: 36922690

Early-stage researchers can greatly benefit from education and skills development in venture creation and commercialization of discoveries.

journal article

LitStream Collection

People

2023 Nature Biotechnology

doi: 10.1038/s41587-023-01706-xpmid: N/A

Recent moves of note in and around the biotech and pharma industries.

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BMC Biotechnology Biotechnology and Biotechnological Equipment Industrial Biotechnology

doi: 10.1038/s41587-022-01487-9pmid: 36302990

N6-methyladenosine (m6A) is the most abundant RNA modification in mammalian cells and the best-studied epitranscriptomic mark. Despite the development of various tools to map m6A, a transcriptome-wide method that enables absolute quantification of m6A at single-base resolution is lacking. Here we use glyoxal and nitrite-mediated deamination of unmethylated adenosines (GLORI) to develop an absolute m6A quantification method that is conceptually similar to bisulfite-sequencing-based quantification of DNA 5-methylcytosine. We apply GLORI to quantify the m6A methylomes of mouse and human cells and reveal clustered m6A modifications with differential distribution and stoichiometry. In addition, we characterize m6A dynamics under stress and examine the quantitative landscape of m6A modification in gene expression regulation. GLORI is an unbiased, convenient method for the absolute quantification of the m6A methylome.

2023

Nature Biotechnology

doi: 10.1038/s41587-022-01471-3pmid: 36203012

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.

Tajes, Juan Fernandez;

Pedersen, Helle Krogh;

Gudmundsdottir, Valborg;

Ridderstråle, Martin;

Walker, Mark;

Forgie, Ian;

Giordano, Giuseppe N.;

Dermitzakis, Emmanouil;

2023 Nature Biotechnology

doi: 10.1038/s41587-022-01520-xpmid: 36593394

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

pmid:

36203014

Methods for in vitro DNA cleavage and molecular cloning remain unable to precisely cleave DNA directly adjacent to bases of interest. Restriction enzymes (REs) must bind specific motifs, whereas wild-type CRISPR–Cas9 or CRISPR–Cas12 nucleases require protospacer adjacent motifs (PAMs). Here we explore the utility of our previously reported near-PAMless SpCas9 variant, named SpRY, to serve as a universal DNA cleavage tool for various cloning applications. By performing SpRY DNA digests (SpRYgests) using more than 130 guide RNAs (gRNAs) sampling a wide diversity of PAMs, we discovered that SpRY is PAMless in vitro and can cleave DNA at practically any sequence, including sites refractory to cleavage with wild-type SpCas9. We illustrate the versatility and effectiveness of SpRYgests to improve the precision of several cloning workflows, including those not possible with REs or canonical CRISPR nucleases. We also optimize a rapid and simple one-pot gRNA synthesis protocol to streamline SpRYgest implementation. Together, SpRYgests can improve various DNA engineering applications that benefit from precise DNA breaks.