Low-input, deterministic profiling of single-cell transcriptomes reveals individual intestinal organoid subtypes comprised of single, dominant cell types [Dispencell]

High-throughput single-cell RNA-sequencing (scRNA-seq) has transformed our ability to resolve cellular properties across systems. A key scRNA-seq catalyzer was the introduction of microdroplet-based systems, which vastly improved sample handling and cell throughput. While powerful, the current microfluidic systems are limited to high cell density (>1000 cells) samples. This prevents the efficient processing of individual, small tissues or rare cells, leading to respectively confounded mosaic cell population read-outs or failed capture of diagnostically interesting cells. In this study, we developed a deterministic, mRNA-capture bead and cell co-encapsulation droplet system, DisCo, that overcomes these limitations by enabling precise particle position and droplet sorting control through combined machine-vision and multilayer microfluidics. We demonstrate that DisCo is capable of processing samples containing few cells (< 100 cells) at high efficiencies( >70%). To underscore the unique capabilities of DisCo, we mapped the developmental process of 31 individual intestinal organoids at the single cell level. This uncovered extensive cellular heterogeneity among organoids, revealing two so far uncharacterized organoid subtypes, “gobloids” and spheroids, predominantly consisting of respectively Muc2+ goblet and Ly6a+ stem cells. Further Disco data analysis thereby revealed strongly increased Yap1 target gene expression in these spheroids, suggesting mechano sensing as the underlying mechanism for their spontaneous formation. Together, our novel “no-cell-left-behind” platform enables the deterministic processing of input cells, allowing high-resolution snapshots of cellular heterogeneity among rare cells or individual, small tissues or organoids.Together, our novel “no-cell-left-behind” platform enables the deterministic processing of input cells, allowing high-resolution snapshots of cellular heterogeneity among rare cells or individual, small tissues or organoids. Overall design: To benchmark single-cell recovery efficiencies throughout the complete DisCo workflow, we sorted HEK 293T cells utilizing the Dispencell pipetting robot. Cells were then processed with DisCo and libraries were prepared in order to quantify cell recovery efficiencies.

高通量单细胞RNA测序（scRNA-seq）彻底革新了我们解析不同系统中细胞特性的能力。推动该技术发展的核心突破之一，便是基于微滴的系统（microdroplet-based systems）的问世，该系统大幅优化了样本处理流程并提升了细胞通量。尽管此类系统性能优异，但当前的微流控系统仅能处理高细胞密度（>1000个细胞）的样本，这使得其无法高效处理单个小型组织或稀有细胞样本，进而分别导致嵌合细胞群体的测序结果出现偏差，或是无法捕获具有临床诊断价值的目标细胞。
本研究中，我们开发了一款确定性mRNA捕获微珠与细胞共包裹微滴系统DisCo，该系统通过结合机器视觉与多层微流控技术，实现对颗粒位置及微滴分选的精准控制，从而克服了上述局限。实验结果表明，DisCo可高效处理细胞数量极少（<100个细胞）的样本，处理效率可达70%以上。
为验证DisCo的独特性能，我们在单细胞层面解析了31个独立小肠类器官的发育过程。该分析揭示了类器官间广泛存在的细胞异质性，并发现了两种此前未被表征的类器官亚型："杯状细胞样类器官（gobloids）"与"球状体（spheroids）"，二者分别主要由Muc2阳性杯状细胞与Ly6a阳性干细胞构成。进一步对DisCo数据的分析显示，此类球状体中Yap1靶基因的表达水平显著上调，提示机械感知是其自发形成的潜在分子机制。
综上，这款全新的"一个都不落下"（no-cell-left-behind）平台可实现对输入细胞的确定性处理，从而实现对稀有细胞、单个小型组织或类器官间细胞异质性的高分辨率快照式解析。综上，这款全新的"一个都不落下"（no-cell-left-behind）平台可实现对输入细胞的确定性处理，从而实现对稀有细胞、单个小型组织或类器官间细胞异质性的高分辨率快照式解析。
整体实验设计：为全面评估DisCo全流程的单细胞回收效率，我们利用Dispencell移液机器人对HEK 293T细胞进行分选，随后通过DisCo系统处理细胞并构建测序文库，以量化细胞回收效率。