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.

开发用于检测蛋白质（或其他分子）的可扩展性，成本效益的抗体库 大大落后于检测（例如杂交或测序）特定RNA或 DNA序列。如果我们能够针对各种目标进行设计抗体并创建大型图书馆 可以通过核酸杂交或测序检测的条形码抗体，许多应用 将能够赋予癌症研究界的能力，包括高维细胞细胞仪（类似 到引用seq），在空间索引测序（例如，使用幻灯片序列，merfish等）原位测序以检测 与靶标（类似于法典）和靶向蛋白质组学结合的抗体（例如Olink接近扩展 测定）。这些应用程序目前依赖于个人（商业）的费力和昂贵的条形码 抗体，然后进行平行检测。在这里，我们建议制定一种新颖的策略，以发现， 工程师并应用合成VHH域纳米构造，以数百至数千个人类蛋白质靶标 同时，可以创建序列定义的抗体，并能够快速生产池 任何研究者大规模的条形码抗体。我们的建议建立了利用我们的新方法 最近开发的无单元平台，用于生产不同的纳米构造的大型库（带有一个输入库 1011-1012复杂性）通过核糖体显示与其编码RNA进行条形码。我们将开发方法 使用这些库并行选择将数百个（与数千）细胞表面结合的纳米词 癌细胞中的蛋白质（即多一对筛查）。我们的方法结合了无细胞的纳米机构工程， 异位ORF表达或CRISPR/CAS9敲除，单细胞测序以实现生成 对付大量靶标的纳米体并行。重要的是，通过一次分析许多目标，我们 第一次方法通过系统地测量靶向结合来验证纳米病的特异性 为每个纳米身体确保结合特异性。我们设想发现和发现的方法 使用针对癌细胞表面分子的纳米型将使高度多路复用的单细胞和空间 组织蛋白质组学。基于拟议的原则证明，可以设想平台使任何实验室能够 开发序列定义的新型检测试剂和高度多路复用的检测试剂 反对许多对癌症研究高价值项目的常规和非常规目标。