Averaging signals over a population of cells often misses biologically relevant variation among single cells. New approaches are emerging with the power to identify differences between individual cells that are not detectable in studies of entire populations. One approach, called single?cell RNA?seq, has rapidly been adopted in the scientific community. Single?cell RNA?seq is now increasingly performed simultaneously with the measurement of protein levels. Recent technologies use DNA?labeled antibodies to measure levels of 10?100 proteins in the context of a genome?wide analysis. However, these technologies have not yet been widely adopted because of the cost and effort required and the relatively small number of proteins that can be measured. Significant cost and effort are required to prepare DNA?labeled antibodies which have limited commercial availability. Furthermore, results with antibodies are sometimes imprecise because antibodies have multiple binding sites for their targets and multiple conjugation sites for their DNA?label. We propose to develop a technology platform optimized for DNA?based detection of proteins in single?cell experiments. The core of our platform is a potent and selective single domain antibody mimetic called a nanoCLAMP that can be customized for specific applications and produced inexpensively. For single?cell applications, nanoCLAMPs have the potential for higher performance and lower cost than traditional antibodies. Unlike traditional antibodies, binding events are straightforward to interpret because nanoCLAMPs are monomers with a single target binding site and a single site for DNA?labeling. nanoCLAMPs are also economical to develop and use because they can be isolated without the need to immunize animals and can be produced in bacteria at high levels. This work proposes 3 specific aims: 1. Generate a test set of 5?10 nanoCLAMPs against cell surface proteins. 2. Optimize DNA labeling and cell?binding conditions. 3. Compare the performance of nanoCLAMPs to traditional antibodies in single?cell experiments and assessthe feasibility of scale?up. Successful completion of these aims will establish the feasibility of a technology platform with the potential to accelerate the adoption of simultaneous single?cell RNA and protein analyses. Public Health Relevance Statement Studying cell populations often misses important variation among individual cells. The proposed work aims to develop low cost, high precision tools that bind specific proteins and allow their measurement in single cells.
Project Terms: Adoption ; Animals ; Antibodies ; Antigens ; immunogen ; Automobile Driving ; driving ; Bacteria ; Binding Sites ; Combining Site ; Reactive Site ; Cells ; Cell Body ; Chemistry ; Communities ; Cysteine ; Half-Cystine ; L-Cysteine ; DNA ; Deoxyribonucleic Acid ; Equipment ; Escherichia coli ; E coli ; E. coli ; Libraries ; Membrane Proteins ; Membrane Protein Gene ; Membrane-Associated Proteins ; Surface Proteins ; Noise ; Oligonucleotides ; Oligo ; oligos ; Proteins ; Publications ; Scientific Publication ; Publishing ; Reagent ; Research ; Messenger RNA ; mRNA ; Signal Transduction ; Cell Communication and Signaling ; Cell Signaling ; Intracellular Communication and Signaling ; Signal Transduction Systems ; Signaling ; biological signal transduction ; Technology ; Testing ; Work ; Generations ; Measures ; Population Heterogeneity ; diverse populations ; heterogeneous population ; population diversity ; Custom ; base ; Label ; Procedures ; Site ; Variant ; Variation ; Biological ; Individual ; Cell Surface Proteins ; Measurement ; tool ; Adopted ; Immunes ; Immune ; Complex ; Event ; Reaction ; monomer ; Performance ; single cell analysis ; success ; Cell surface ; RNA analysis ; Genomics ; mimetics ; Molecular Interaction ; Binding ; Length ; Data ; Detection ; Protein Analysis ; Phage Display ; Development ; developmental ; cost ; design ; designing ; novel strategies ; new approaches ; novel approaches ; novel strategy ; scale up ; Population ; genome-wide analysis ; genome wide analysis ; genome wide studies ; genome-wide identification ; genome-wide ; genome scale ; genomewide ; nanobodies ; nanobody ; sdAb ; single domain antibodies ; transcriptome sequencing ; RNA Seq ; RNA sequencing ; RNAseq ; next generation sequencing ; NGS Method ; NGS system ; next gen sequencing ; nextgen sequencing ; multiple omics ; multiomics ; experimental study ; experiment ; experimental research ; Immunize ; single-cell RNA sequencing ; scRNA-seq ; single cell RNA-seq ; single cell RNAseq ; cost estimate ; cost estimation ; antibody detection ; antibody based detection ; detect antibodies ; Cellular Indexing of Transcriptomes and Epitopes by Sequencing ; CITE sequencing ; CITE-seq ; CITEseq ; cellular indexing of transcriptomes and epitopes by single cell sequencing ;
