來(lái)源于 Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

對(duì)比了13 commonly used scRNA-seq and single-nucleus RNA-seq的方法對(duì)比，也算是各有千秋研乒。結(jié)果來(lái)看繁成，不盡相同佛致。在采用方法策略時(shí)候袭灯，還是要結(jié)合自己的課題煤搜，選擇合適的方法坝初，不能亂來(lái)咪笑。

摘要

單細(xì)胞RNA測(cè)序(scRNA-seq)是一項(xiàng)用于分辨樣本中單細(xì)胞水平轉(zhuǎn)錄組的領(lǐng)先技術(shù)可帽。最新的一些protocols可hold住成千上萬(wàn)級(jí)別單細(xì)胞的測(cè)序，并已經(jīng)被用于展示組織器官和生物體水平的cell atlases窗怒。然而映跟，這些不同的protocols在RNA捕獲效率、捕獲偏倚程度扬虚、單細(xì)胞規(guī)模和建庫(kù)成本方面存在很大差異努隙，它們?cè)诓煌瑧?yīng)用方向中的相對(duì)優(yōu)劣性尚不很清楚。
本研究生成了一個(gè)基準(zhǔn)數(shù)據(jù)集孔轴，用以系統(tǒng)地評(píng)估這些單細(xì)胞測(cè)序的protocols在全面單細(xì)胞類型分辨能力和狀態(tài)方面的能力剃法。我們進(jìn)行了一項(xiàng)多中心研究，用混合多種細(xì)胞的異質(zhì)參考樣本路鹰，對(duì)13種常用的scRNA-seq和單核RNA-seq protocol 進(jìn)行了評(píng)測(cè)贷洲。比較分析顯示各個(gè)protocols性能有顯著差異。這些protocols在文庫(kù)的復(fù)雜性和檢測(cè)細(xì)胞類型markers的能力上有所不同晋柱，這些指標(biāo)影響了它們的預(yù)測(cè)值和整合到 reference cell atlases的普適性优构。本結(jié)果為研究人員和聯(lián)合項(xiàng)目(如人類細(xì)胞圖譜Human Cell Atlas)提供了指導(dǎo)守則。

Abstract

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear.
In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.

Fig. 1: Overview of the experimental design and data processing.

測(cè)試樣品是一個(gè)包含人雁竞、鼠钦椭、狗細(xì)胞的混合細(xì)胞樣品拧额，用于測(cè)試13種單細(xì)胞測(cè)序方案。獲得的reads分別mapping到人彪腔、鼠侥锦、犬的參考序列上，分別計(jì)算不同物種的不同測(cè)序方法的基因表達(dá)量德挣。
The reference sample consists of human PBMCs (60%), and HEK293T (6%), mouse colon (30%), NIH3T3 (3%) and dog MDCK cells (1%). The sample was prepared in one single batch, cryopreserved and sequenced by 13 different sc/snRNA-seq methods. Sequences were uniformly mapped to a joint human, mouse and canine reference, and then separately to produce gene expression counts for each sequencing method.

Fig. 2: Comparison of 13 sc/snRNA-seq methods.

a, Color legend of sc/snRNA-seq protocols.
b, 人細(xì)胞UMAP of 30,807 cells from the human reference sample (Chromium) colored by cell-type annotation.
c, 鼠細(xì)胞UMAP of 19,749 cells from the mouse reference (Chromium) colored by cell-type annotation.
d, Boxplots displaying the minimum, the first, second and third quantiles, and the maximum number of genes detected across the protocols, in down-sampled (20,000) HEK293T cells, monocytes and B cells. Cell identities were defined by combining the clustering of each dataset and cell projection on to the reference.
e, Number of detected genes at stepwise. down-sampled, sequencing depths. Points represent the average number of detected genes as a fraction of all cells of the corresponding cell type at the corresponding sequencing depth.
f, Dropout probabilities as a function of expression magnitude, for each protocol and cell type, calculated on down-sampled data (20,000) for 50 randomly selected cells.

Fig. 3: Similarity measures of sc/snRNA-seq methods.

a,b, Principal component analysis on down-sampled data (20,000) using highly variable genes between protocols, separated into HEK293T cells, monocytes and B cells, and color coded by protocol (a) and number of detected genes per cell (b).
c, Pearson’s correlation plots across protocols using expression of common genes. For a fair comparison, cells were down-sampled to the same number for each method (B cells, n?=?32; monocytes, n?=?57; HEK293T cells, n?=?55). Protocols are ordered by agglomerative hierarchical clustering.
d, Average log(expression) values of cell-type-specific reference markers for down-sampled (20,000) HEK293T cells, monocytes and B cells.
e, Log(expression) values of reference markers on down-sampled data (20,000) for HEK293T cells, monocytes and B cells (maximum of 50 random cells per technique).
f, Cumulative gene counts per protocol as the average of 100 randomly sampled HEK293T cells, monocytes and B cells, separately on down-sampled data (20,000).

Fig. 4: Clustering analysis of 13 sc/snRNA-seq methods on down-sampled datasets (20,000).

a, The tSNE visualizations of unsupervised clustering in human samples from 13 different methods. Each dataset was analyzed separately after down-sampling to 20,000?reads?per cell. Cells are colored by cell type inferred by matchSCore2 before down-sampling. Cells that did not achieve a probability score of 0.5 for any cell type were considered unclassified.
b, Clustering accuracy and ASW for clusters in each protocol.

Fig. 5: Integration of sc/snRNA-seq methods.

a–d, UMAP visualization of cells after integrating technologies for 18,034 human (a,b) and 7,902 mouse (c,d) cells. Cells are colored by cell type (a,c) and sc/snRNA-seq protocol (b,d).
e,f, Barplots showing normalized and method-corrected (integrated) expression scores of cell-type-specific signatures for human HEK293T cells, monocytes, B cells (e), and mouse secretory and TA cells (f). Bars represent cells and colors methods.
g,h, Evaluation of method integratability in human (g) and mouse (h) cells. Protocols are compared according to their ability to group cell types into clusters (after integration) and mix with other technologies within the same clusters. Points are colored by sequencing method.

Fig. 6: Benchmarking summary of 13 sc/snRNA-seq methods.

Methods are scored by key analytical metrics, characterizing protocols according to their ability to recapitulate the original structure of complex tissues, and their suitability for cell atlas projects. The methods are ordered by their overall benchmarking score, which is computed by averaging the scores across metrics assessed from the human datasets.

參考文獻(xiàn)：

• Analysis
• Published: 2020-04-06

Benchmarking single-cell RNA-sequencing protocols for cell atlas projects

Elisabetta Mereu, Atefeh Lafzi Holger Heyn*
Nature Biotechnology volume 38, pages747–755(2020)Cite this article