Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia

Abstract

2Current leukemia therapies target cancer cells with specific phenotypes or genotypes, but this 3 assumes that either genomic mutations or immunophenotypes alone serve as faithful proxies for 4 treatment response 1 . Moreover, the heterogeneity inherent to all cancers, including leukemias, 5 makes direct mapping of genotype-phenotype relationships challenging 2,3 . Here, we present a 6 method to genotype and phenotype single cells at high throughput, allowing direct characterization 7 of proteogenomic states on tens of thousands of cancer cells rapidly and cost efficiently. Using the 8 approach, we analyze the disease of three leukemia patients over multiple treatment timepoints 9 and recurrences. We observe complex genotype-phenotype dynamics and extensive decoupling of 10 the relationships over disease progression and response to therapy, illustrating the subtlety of the 11 disease process and the inability to use genotypes as direct proxies for phenotypes. Our technology 12 has enabled the first rigorous test of the prevailing paradigm that treatment of a disease phenotype 13 is equivalent to treatment of its underlying genotype. More broadly, our results highlight the power 14 of single-cell multiomic measurements to resolve complex biology in heterogeneous populations, 15 and illustrate how this information can be used to inform treatment. We thus expect that our 16 methodology will find broad application to study proteogenomic tumor landscapes across cancers 17 and will support the next generation of immunotherapy. 18 19 20 Main 21 22 42 aberrations such as single nucleotide polymorphisms (SNPs) and gene fusions 13 . Single-cell RNA 43 sequencing (scRNA-seq) has emerged as a potentially valuable approach for genotype-phenotype 44 linkage because it is cost effective and scalable 3,10,14,15 . The mRNA sequences provide genotype 45 information 15,16 while their counts relate phenotype 17-21 . Moreover, modern approaches are 46 extremely high throughput, allowing characterization of thousands of cells. Nevertheless, 47

Single-cell mutation analysis of clonal evolution in myeloid malignancies

“…We next investigated if specific mutations or combinations influenced immunophenotypes. We performed simultaneous scDNA-seq and cell surface protein expression analysis 17 on CH patients with ≥1 mutation(s) to investigate the contribution of CH mutations to mature hematopoietic lineages. We observed differential B- and T-cell lineage contribution depending on the mutated CH allele.…”

Modular barcode beads for microfluidic single cell genomics

“…Read 1 and read 2 sequences were demultiplexed into barcode groups and valid cell barcode groups were discriminated from background barcode groups by identifying the inflection point of the barcode rank plot versus number of associated reads. Reads from valid cell barcodes were processed as previously described( 12 ). Briefly, FASTQ files with valid reads were aligned to the hg19 build of the human genome reference using bowtie2 (v2.3.4.1), filtered (properly mapped, mapping quality > 2, primary alignment), sorted, and indexed with samtools (v1.8).…”

“…Here, we describe a modular framework that allows easy repurposing of existing bead batches to new targets ( Figure 1b, 1c)(supplementary protocol). The modular design is compatible with described single cell sequencing workflows, including scDNAseq (7), scRNAseq (2), Abseq (8), multimodal analyses (9)(10)(11)(12), and genome wide knockout screens (13)(14)(15). Moreover, the optimized structure generates compact barcodes that can be assembled in a fraction of the time and cost compared to existing methods.…”

Time to Move to the Single-Cell Level: Applications of Single-Cell Multi-Omics to Hematological Malignancies and Waldenström’s Macroglobulinemia—A Particularly Heterogeneous Lymphoma

“…Single-Cell DNA and Antibody Sequencing (DAb-seq) is a method that integrates DNA profiling and surface proteins of single cells at high throughput, allowing the proteogenomic dynamics of multiple patients to be tracked over multiple treatments and recurrences. Analyzing leukemia patients using this approach identified extensive genotype–phenotype decoupling, with immunophenotypic heterogeneity among cells with the same pathogenic mutation, as well as genotypically diverse cells with a convergent malignant immunophenotype, suggesting that independent phenotype or genotype measurements do not adequately capture the full extent of proteogenomic heterogeneity [ 23 ].…”