Nature Biotechnology

Prime editing (PE) applications are limited by low editing efficiency. Here we show that designing prime binding sites with a melting temperature of 30?°C leads to optimal performance in rice and that using two prime editing guide (peg) RNAs in trans encoding the same edits substantially enhances PE efficiency. Together, these approaches boost PE efficiency from 2.9-fold to 17.4-fold. Optimal pegRNAs or pegRNA pairs can be designed with our web application, PlantPegDesigner.

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Engineering luminescent biosensors for point-of-care SARS-CoV-2 antibody detection

Susanna K. Elledge, Xin X. Zhou, James R. Byrnes, Alexander J. Martinko, Irene Lui, Katarina Pance, Shion A. Lim, Jeff E. Glasgow, Anum A. Glasgow, Keirstinne Turcios, Nikita S. Iyer, Leonel Torres, Michael J. Peluso, Timothy J. Henrich, Taia T. Wang, Cristina M. Tato, Kevin K. Leung, Bryan Greenhouse & James A. Wells

doi : 10.1038/s41587-021-00878-8

Nature Biotechnology volume 39, pages928–935 (2021)

Current serology tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies mainly take the form of enzyme-linked immunosorbent assays, chemiluminescent microparticle immunoassays or lateral flow assays, which are either laborious, expensive or lacking sufficient sensitivity and scalability. Here we present the development and validation of a rapid, low-cost, solution-based assay to detect antibodies in serum, plasma, whole blood and to a lesser extent saliva, using rationally designed split luciferase antibody biosensors. This new assay, which generates quantitative results in 30?min, substantially reduces the complexity and improves the scalability of coronavirus disease 2019 (COVID-19) antibody tests. This assay is well-suited for point-of-care, broad population testing, and applications in low-resource settings, for monitoring host humoral responses to vaccination or viral infection.

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Compressed sensing for highly efficient imaging transcriptomics

Brian Cleary, Brooke Simonton, Jon Bezney, Evan Murray, Shahul Alam, Anubhav Sinha, Ehsan Habibi, Jamie Marshall, Eric S. Lander, Fei Chen & Aviv Regev

doi : 10.1038/s41587-021-00883-x

Nature Biotechnology volume 39, pages936–942 (2021)

Recent methods for spatial imaging of tissue samples can identify up to ~100 individual proteins1,2,3 or RNAs4,5,6,7,8,9,10 at single-cell resolution. However, the number of proteins or genes that can be studied in these approaches is limited by long imaging times. Here we introduce Composite In Situ Imaging (CISI), a method that leverages structure in gene expression across both cells and tissues to limit the number of imaging cycles needed to obtain spatially resolved gene expression maps. CISI defines gene modules that can be detected using composite measurements from imaging probes for subsets of genes. The data are then decompressed to recover expression values for individual genes. CISI further reduces imaging time by not relying on spot-level resolution, enabling lower magnification acquisition, and is overall about 500-fold more efficient than current methods. Applying CISI to 12 mouse brain sections, we accurately recovered the spatial abundance of 37 individual genes from 11 composite measurements covering 180?mm2 and 476,276 cells.

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MHC class II tetramers engineered for enhanced binding to CD4 improve detection of antigen-specific T cells

Thamotharampillai Dileepan, Deepali Malhotra, Dmitri I. Kotov, Elizabeth M. Kolawole, Peter D. Krueger, Brian D. Evavold & Marc K. Jenkins

doi : 10.1038/s41587-021-00893-9

Nature Biotechnology volume 39, pages943–948 (2021)

The ability to identify T cells that recognize specific peptide antigens bound to major histocompatibility complex (MHC) molecules has enabled enumeration and molecular characterization of the lymphocytes responsible for cell-mediated immunity. Fluorophore-labeled peptide:MHC class I (p:MHCI) tetramers are well-established reagents for identifying antigen-specific CD8+ T cells by flow cytometry, but efforts to extend the approach to CD4+ T cells have been less successful, perhaps owing to lower binding strength between CD4 and MHC class II (MHCII) molecules. Here we show that p:MHCII tetramers engineered by directed evolution for enhanced CD4 binding outperform conventional tetramers for the detection of cognate T cells. Using the engineered tetramers, we identified about twice as many antigen-specific CD4+ T cells in mice immunized against multiple peptides than when using traditional tetramers. CD4 affinity-enhanced p:MHCII tetramers, therefore, allow direct sampling of antigen-specific CD4+ T cells that cannot be accessed with conventional p:MHCII tetramer technology. These new reagents could provide a deeper understanding of the T cell repertoire.

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In vivo adenine base editing of PCSK9 in macaques reduces LDL cholesterol levels

Tanja Rothgangl, Melissa K. Dennis, Paulo J. C. Lin, Rurika Oka, Dominik Witzigmann, Lukas Villiger, Weihong Qi, Martina Hruzova, Lucas Kissling, Daniela Lenggenhager, Costanza Borrelli, Sabina Egli, Nina Frey, Noëlle Bakker, John A. Walker II, Anastasia P. Kadina, Denis V. Victorov, Martin Pacesa, Susanne Kreutzer, Zacharias Kontarakis, Andreas Moor, Martin Jinek, Drew Weissman, Markus Stoffel, Ruben van Boxtel, Kevin Holden, Norbert Pardi, Beat Th?ny, Johannes H?berle, Ying K. Tam, Sean C. Semple & Gerald Schwank

doi : 10.1038/s41587-021-00933-4

Nature Biotechnology volume 39, pages949–957 (2021)

Most known pathogenic point mutations in humans are C•G to T•A substitutions, which can be directly repaired by adenine base editors (ABEs). In this study, we investigated the efficacy and safety of ABEs in the livers of mice and cynomolgus macaques for the reduction of blood low-density lipoprotein (LDL) levels. Lipid nanoparticle–based delivery of mRNA encoding an ABE and a single-guide RNA targeting PCSK9, a negative regulator of LDL, induced up to 67% editing (on average, 61%) in mice and up to 34% editing (on average, 26%) in macaques. Plasma PCSK9 and LDL levels were stably reduced by 95% and 58% in mice and by 32% and 14% in macaques, respectively. ABE mRNA was cleared rapidly, and no off-target mutations in genomic DNA were found. Re-dosing in macaques did not increase editing, possibly owing to the detected humoral immune response to ABE upon treatment. These findings support further investigation of ABEs to treat patients with monogenic liver diseases.

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Affinity-matured HLA class II dimers for robust staining of antigen-specific CD4+ T cells

Kenji Sugata, Yukiko Matsunaga, Yuki Yamashita, Munehide Nakatsugawa, Tingxi Guo, Levon Halabelian, Yota Ohashi, Kayoko Saso, Muhammed A. Rahman, Mark Anczurowski, Chung-Hsi Wang, Kenji Murata, Hiroshi Saijo, Yuki Kagoya, Dalam Ly, Brian D. Burt, Marcus O. Butler, Tak W. Mak & Naoto Hirano

doi : 10.1038/s41587-021-00836-4

Nature Biotechnology volume 39, pages958–967 (2021)

Peptide–major histocompatibility complex (pMHC) multimers enable the detection of antigen-specific T cells in studies ranging from vaccine efficacy to cancer immunotherapy. However, this technology is unreliable when applied to pMHC class II for the detection of CD4+ T cells. Here, using a combination of molecular biological and immunological techniques, we cloned sequences encoding human leukocyte antigen (HLA)-DP, HLA-DQ and HLA-DR molecules with enhanced CD4 binding affinity (with a Kd of 8.9?±?1.1?µM between CD4 and affinity-matured HLA-DP4) and produced affinity-matured class II dimers that stain antigen-specific T cells better than conventional multimers in both in vitro and ex vivo analyses. Using a comprehensive library of dimers for HLA-DP4, which is the most frequent HLA allele in many ancestry groups, we mapped 103 HLA-DP4-restricted epitopes derived from diverse tumor-associated antigens and cloned the cognate T-cell antigen receptor (TCR) genes from in vitro-stimulated CD4+ T cells. The availability of affinity-matured class II dimers across HLA-DP, HLA-DQ and HLA-DR alleles will aid in the investigation of human CD4+ T-cell responses.

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Spatial tissue profiling by imaging-free molecular tomography

Halima Hannah Schede, Christian G. Schneider, Johanna Stergiadou, Lars E. Borm, Anurag Ranjak, Tracy M. Yamawaki, Fabrice P. A. David, Peter L?nnerberg, Maria Antonietta Tosches, Simone Codeluppi & Gioele La Manno

doi : 10.1038/s41587-021-00879-7

Nature Biotechnology volume 39, pages968–977 (2021)

Several techniques are currently being developed for spatially resolved omics profiling, but each new method requires the setup of specific detection strategies or specialized instrumentation. Here we describe an imaging-free framework to localize high-throughput readouts within a tissue by cutting the sample into thin strips in a way that allows subsequent image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (called STRP-seq) and validated it by profiling the spatial transcriptome of the mouse brain. We applied the technique to the brain of the Australian bearded dragon, Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. We expect that STRP-seq can be used to derive spatially resolved data from a range of other omics techniques.

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Quantitative mapping of the cellular small RNA landscape with AQRNA-seq

Jennifer F. Hu, Daniel Yim, Duanduan Ma, Sabrina M. Huber, Nick Davis, Jo Marie Bacusmo, Sidney Vermeulen, Jieliang Zhou, Thomas J. Begley, Michael S. DeMott, Stuart S. Levine, Valérie de Crécy-Lagard, Peter C. Dedon & Bo Cao

doi : 10.1038/s41587-021-00874-y

Nature Biotechnology volume 39, pages978–988 (2021)

Current next-generation RNA-sequencing (RNA-seq) methods do not provide accurate quantification of small RNAs within a sample, due to sequence-dependent biases in capture, ligation and amplification during library preparation. We present a method, absolute quantification RNA-sequencing (AQRNA-seq), that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for all small RNAs in a sample. Library preparation and data processing were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying length, and RNA blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800?detectable miRNAs that varied during cancer progression, while application to bacterial transfer RNA pools, with the challenges of secondary structure and abundant modifications, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced, tRNA-driven, codon-biased translation. AQRNA-seq thus provides a versatile means to quantitatively map the small RNA landscape in cells.

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Generation of recombinant hyperimmune globulins from diverse B-cell repertoires

Sheila M. Keating, Rena A. Mizrahi, Matthew S. Adams, Michael A. Asensio, Emily Benzie, Kyle P. Carter, Yao Chiang, Robert C. Edgar, Bishal K. Gautam, Ashley Gras, Jackson Leong, Renee Leong, Yoong Wearn Lim, Vishal A. Manickam, Angelica V. Medina-Cucurella, Ariel R. Niedecken, Jasmeen Saini, Jan Fredrik Simons, Matthew J. Spindler, Kacy Stadtmiller, Brendan Tinsley, Ellen K. Wagner, Nicholas Wayham, LaRee Tracy, Carina Vingsbo Lundberg, Dirk Büscher, Jose Vicente Terencio, Lucy Roalfe, Emma Pearce, Hayley Richardson, David Goldblatt, Anushka T. Ramjag, Christine V. F. Carrington, Graham Simmons, Marcus O. Muench, Steven M. Chamow, Bryan Monroe, Charles Olson, Thomas H. Oguin, Heather Lynch, Robert Jeanfreau, Rachel A. Mosher, Matthew J. Walch, Christopher R. Bartley, Carl A. Ross, Everett H. Meyer, Adam S. Adler & David S. Johnson

doi : 10.1038/s41587-021-00894-8

Nature Biotechnology volume 39, pages989–999 (2021)

Plasma-derived polyclonal antibody therapeutics, such as intravenous immunoglobulin, have multiple drawbacks, including low potency, impurities, insufficient supply and batch-to-batch variation. Here we describe a microfluidics and molecular genomics strategy for capturing diverse mammalian antibody repertoires to create recombinant multivalent hyperimmune globulins. Our method generates of diverse mixtures of thousands of recombinant antibodies, enriched for specificity and activity against therapeutic targets. Each hyperimmune globulin product comprised thousands to tens of thousands of antibodies derived from convalescent or vaccinated human donors or from immunized mice. Using this approach, we generated hyperimmune globulins with potent neutralizing activity against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in under 3?months, Fc-engineered hyperimmune globulins specific for Zika virus that lacked antibody-dependent enhancement of disease, and hyperimmune globulins specific for lung pathogens present in patients with primary immune deficiency. To address the limitations of rabbit-derived anti-thymocyte globulin, we generated a recombinant human version and demonstrated its efficacy in mice against graft-versus-host disease.

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Iterative single-cell multi-omic integration using online learning

Chao Gao, Jialin Liu, April R. Kriebel, Sebastian Preissl, Chongyuan Luo, Rosa Castanon, Justin Sandoval, Angeline Rivkin, Joseph R. Nery, Margarita M. Behrens, Joseph R. Ecker, Bing Ren & Joshua D. Welch

doi : 10.1038/s41587-021-00867-x

Nature Biotechnology volume 39, pages1000–1007 (2021)

Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.

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Bayesian inference of gene expression states from single-cell RNA-seq data

Jérémie Breda, Mihaela Zavolan & Erik van Nimwegen

doi : 10.1038/s41587-021-00875-x

Nature Biotechnology volume 39, pages1008–1016 (2021)

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

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Publisher Correction: Voices of biotech leaders

Katrine Bosley, Charlotte Casebourn, Priscilla Chan, Janice Chen, Michael Chen, George Church, John Cumbers, Tomas de Wouters, Heather Dewey-Hagborg, Xavier Duportet, Abasi Ene-Obong, Arturo Elizondo, Jeremy Farrar, Bill Gates, Francesco Gatto, Sebastian Giwa, Jernej Godec, Silvia Gold, Emily LeProust, Jeantine Lunshof, Eddie Martucci, Michelle McMurray Heath, Jason Mellad, Veronika Oudova, Neri Oxman, Aviv Regev, Sarah Richardson, Christopher Thomas Scott, Jake Sherkow, Leah Sibener, Teresa Tarrag?, Sharon Terry, J. Craig Venter, Spin Wang, Sajith Wickramasekara, Hakim Yadi, Luhan Yang & Bowen Zhao

doi : 10.1038/s41587-021-01000-8

Nature Biotechnology volume 39, page1017 (2021)

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