Development of methods for highly multiplexed quantification of cancer proteomes using large-scale nanobody libraries

使用大规模纳米抗体库开发癌症蛋白质组高度多重定量的方法

• 批准号: 10714023
• 负责人: Nir Hacohen
• 金额: $ 22.16万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-09 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714023
• 关键词: Affinity; Animals; Antibodies; Antibody Specificity; Antigens; Area; Artificial Membranes; Bacteria; Bacteriophages; Bar Codes; Binding; Biological Assay; Biology; CRISPR/Cas technology; Cancer Biology; Cancer Detection; Cancer cell line; Cell Surface Proteins; Cell surface; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Communities; Complex; Consumption; Cytometry; DNA Library; DNA Sequence; Dependence; Detection; Development; Diagnostic; Engineering; Ensure; Epitopes; Family; G-Protein-Coupled Receptors; Generations; Genotype; High-Throughput Nucleotide Sequencing; Human; In Vitro; Individual; Integral Membrane Protein; Knock-out; Libraries; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measurement; Measures; Medicine; Membrane Proteins; Methodology; Methods; Molecular Target; Nanotechnology; Nucleic Acid Hybridization; Open Reading Frames; Phenotype; Production; Proteins; Proteome; Proteomics; RNA Sequences; Rapid screening; Reagent; Research Personnel; Resources; Ribosomal RNA; Ribosomes; SARS-CoV-2 spike protein; Slide; Solubility; Specificity; Surface; System; Targeted Research; Technology; Testing; Therapeutic; Time; Tissues; Validation; Virus; antibody detection; antibody engineering; antibody libraries; anticancer research; cancer cell; cell dimension; cell growth; cost effective; cost efficient; design; detection method; empowerment; flexibility; high dimensionality; in situ sequencing; indexing; interest; method development; nanobodies; nanoscale; new technology; novel; novel strategies; nucleic acid detection; protein profiling; receptor binding; screening; single cell sequencing; tool

The development of scalable, cost-effective antibody libraries for detecting proteins (or other molecules) has greatly lagged the development of methods for detecting (e.g. by hybridization or sequencing) specific RNA or DNA sequences. If we were able to engineer antibodies against diverse targets and create large-scale libraries of barcoded antibodies that could be detected by nucleic acid hybridization or sequencing, many applications would be enabled to empower the cancer research community, including high-dimensional cell cytometry (similar to CITE-seq), spatially-indexed sequencing (e.g. using Slide-seq, MERFISH, etc), in situ sequencing to detect antibodies bound to targets (similar to CODEX) and targeted proteomics (such as the Olink proximity extension assay). These applications currently rely on laborious and expensive barcoding of individual (commercial) antibodies, followed by pooling for parallel detection. Here, we propose to develop a novel strategy to discover, engineer and apply synthetic VHH-domain nanobodies against hundreds to thousands of human protein targets simultaneously, thus enabling creation of sequence-defined antibodies and enabling rapid production of pools of barcoded-antibodies at large scale by any investigator. Our proposal builds novel methods that leverage our recently developed cell-free platform for producing large libraries of distinct nanobodies (with an input library of 1011-1012 complexity) that are barcoded with their encoding RNA by ribosome display. We will develop methods to use these libraries for parallel selection of nanobodies that bind each of hundreds (to thousands) of cell surface proteins (i.e. a many-to-many screen) in cancer cells. Our approach combines cell-free nanobody engineering, ectopic ORF expression or CRISPR/Cas9 knockout, with single cell sequencing to achieve generation of nanobodies against a large number of targets in parallel. Importantly, by analyzing many targets at once, our approach for the first time validates nanobody specificity at scale by measuring off-target binding systematically for each nanobody to ensure binding specificity. We envision that the resulting methods for discovering and using nanobodies against cancer cell surface molecules would enable highly multiplexed single cell and spatial tissue proteomics. Building on the proposed proof of principle, one can envision the platform enabling any lab to develop sequence-defined novel detection reagents and highly multiplexed libraries of detection reagents against conventional and unconventional targets across many projects of high value to cancer research.

用于检测蛋白质（或其他分子）的可扩展的、成本有效的抗体文库的开发已经 大大滞后于用于检测（例如通过杂交或测序）特异性RNA或RNA片段的方法的发展。 DNA序列如果我们能够设计针对不同目标的抗体并创建大规模的文库， 条形码抗体可以通过核酸杂交或测序检测，许多应用 将能够赋予癌症研究界，包括高维细胞计数（类似于 到CITE-seq）、空间索引测序（例如使用Slide-seq、MERFISH等）、原位测序以检测 结合靶点的抗体（类似于CODEX）和靶向蛋白质组学（如Olink邻近延伸 测定）。这些应用程序目前依赖于费力和昂贵的条形码的个人（商业） 抗体，然后合并用于平行检测。在这里，我们建议开发一种新的策略来发现， 设计并应用合成的VHH结构域纳米抗体对抗数百至数千个人类蛋白质靶标 同时，从而能够产生序列限定的抗体，并能够快速产生抗体库。 条形码抗体在大规模的任何研究者。我们的提案建立了新的方法， 最近开发的用于产生不同纳米抗体的大文库的无细胞平台（具有不同纳米抗体的输入文库）。 1011-1012复杂性），其通过核糖体展示用其编码RNA条形码化。我们将开发方法 使用这些文库来平行选择结合数百（至数千）细胞表面的每一个的纳米抗体 蛋白质（即多对多筛选）。我们的方法结合了无细胞纳米抗体工程， 异位ORF表达或CRISPR/Cas9敲除，用单细胞测序来实现 纳米抗体对大量的目标平行。重要的是，通过同时分析多个目标， 一种方法首次通过系统地测量脱靶结合来验证纳米抗体的特异性 以确保结合特异性。我们设想，由此产生的方法，发现和 使用针对癌细胞表面分子的纳米抗体将使高度多路复用的单细胞和空间 组织蛋白质组学基于提出的原理证明，可以设想该平台使任何实验室都能够 开发序列确定的新型检测试剂和高度多路复用的检测试剂库 针对传统和非传统的目标，在许多对癌症研究有高价值的项目中。