Illumina公司的基本觀點(diǎn)直接明確：生物十分復(fù)雜，為了揭示生物復(fù)雜性并借此對(duì)人類醫(yī)學(xué)產(chǎn)生重要影響,，需要大幅提升試驗(yàn)規(guī)模、大量削減每次試驗(yàn)的成本,。某種程度上,，這種理念成功了。

20年來(lái),，Illumina通過(guò)大力發(fā)展小型化多重分析,，為基因組學(xué)革命注入了動(dòng)力。無(wú)論是一開(kāi)始的陣列,，還是現(xiàn)在的新一代基因測(cè)序,，都在納米級(jí)別的實(shí)驗(yàn)中并列進(jìn)行了數(shù)十億次分析。這些工具及其應(yīng)用為實(shí)現(xiàn)精準(zhǔn)醫(yī)療帶來(lái)希望,，正在徹底改變整個(gè)醫(yī)療領(lǐng)域的實(shí)踐,。

2011年底推出了非侵入性產(chǎn)前檢測(cè)，僅僅過(guò)了六年,，每年檢測(cè)數(shù)就高達(dá)200多萬(wàn)次,。通過(guò)活組織檢查或游離細(xì)胞分析進(jìn)行腫瘤測(cè)序的方法正在改變癌癥的診斷和治療。改變已經(jīng)足夠明顯,，2018年首次批準(zhǔn)了以腫瘤遺傳特征（NTRK基因融合）而非傳統(tǒng)的癌癥起源界定（肺癌,、結(jié)腸癌等）為基礎(chǔ)的治療型藥物。

然而二十年后,，精準(zhǔn)醫(yī)療在很大程度上仍然只是希望,。生物仍然非常復(fù)雜。似乎每當(dāng)我們解開(kāi)了其中的一部分復(fù)雜性時(shí),，就會(huì)發(fā)現(xiàn)更大范圍的復(fù)雜性,。

當(dāng)我們開(kāi)始了解某一特定基因的功能時(shí)，我們就需要了解該基因的網(wǎng)絡(luò),，了解其RNA和蛋白質(zhì)的翻譯后修飾及其綜合細(xì)胞調(diào)節(jié)機(jī)制,，才有可能提出我們真正想問(wèn)的問(wèn)題：上述所有因素如何作用于疾病。

將整個(gè)人類基因組的測(cè)序成本從超過(guò)10億美元降至1000美元以下,，推動(dòng)我們?cè)谶@個(gè)還遠(yuǎn)沒(méi)有答完的難題上取得了巨大進(jìn)步,。如果將成本降至100美元，會(huì)有更多發(fā)現(xiàn),；然而,，這些發(fā)現(xiàn)將是增補(bǔ)性的而非革命性的?？茖W(xué)家在拓寬人類知識(shí)庫(kù)的過(guò)程中,，要求他們使用的工具采用了正交法,，具有新功能。我們認(rèn)為有兩種方法符合該標(biāo)準(zhǔn)：分辨率的顯著提升,、曾經(jīng)獨(dú)立的學(xué)科和能力進(jìn)行整合,。

分辨率革命：?jiǎn)渭?xì)胞基因組學(xué)

直到前不久，基因組學(xué)還一直依賴于大塊組織樣本的分析,。雖然這種方式可以回答一些重要問(wèn)題,，但忽略了關(guān)鍵的生物信息。任何活組織檢查都包含多種細(xì)胞類型,。

人們已經(jīng)知道（而且還在計(jì)數(shù)）有200余種人體細(xì)胞類型。把樣本中所有細(xì)胞混在一起進(jìn)行整體分析會(huì)導(dǎo)致不同細(xì)胞的功能被混為一談,，也會(huì)忽略不同細(xì)胞類型的比例差異,。這種分析只能揭示最明顯的信息。單細(xì)胞基因組學(xué)——能夠在全基因組范圍內(nèi)單獨(dú)分析每個(gè)細(xì)胞——為我們了解生物復(fù)雜性提供了有力的透鏡,。

10x Genomics（以及其他公司）正在開(kāi)創(chuàng)單細(xì)胞基因組學(xué)的新方法,，擴(kuò)展可以在單細(xì)胞水平上進(jìn)行的分析類型，包括DNA測(cè)序,、RNA分析,、表觀遺傳學(xué)和免疫學(xué)等。由于這類平臺(tái)意義重大且發(fā)展迅猛,，在整個(gè)研究生態(tài)中的應(yīng)用也愈加廣泛,，今后必將開(kāi)發(fā)出更多單細(xì)胞分析的應(yīng)用方式。

單細(xì)胞基因組學(xué)在細(xì)胞層面的洞察力才開(kāi)始真正展示出這種方法的力量,；例如,，新發(fā)現(xiàn)了一種罕見(jiàn)的呼吸道細(xì)胞類型（肺離子細(xì)胞），這種細(xì)胞被認(rèn)為在囊性纖維化中發(fā)揮了重要作用,。

即便如此,，甚至還有其它方面的信息可以強(qiáng)化實(shí)驗(yàn)——這些細(xì)胞在組織中的位置和相互作用。這類空間信息對(duì)于理解某些疾病至關(guān)重要,。

傳統(tǒng)上,，組織分析一直屬于病理學(xué)范疇——用有一兩個(gè)標(biāo)記的薄片。未來(lái)工具開(kāi)發(fā)的一個(gè)重要領(lǐng)域?qū)⑹菍?duì)具有完整細(xì)胞空間定位的組織進(jìn)行基因組層面的分析,，從而在組織層面上對(duì)細(xì)胞進(jìn)行基因組分析,。

整合的力量

盡管整合DNA、RNA和蛋白質(zhì)數(shù)據(jù)的想法已經(jīng)討論了十多年,，卻囿于數(shù)據(jù)分辨率不足和統(tǒng)一分析技術(shù)的缺乏,。但是最近的一些進(jìn)展正在突破這些局限；現(xiàn)在可以對(duì)同一樣本在單細(xì)胞層面上分析其RNA轉(zhuǎn)錄和表觀遺傳變化,，從而深入了解表觀遺傳學(xué)如何影響轉(zhuǎn)錄,。

同樣,，可以在單細(xì)胞層面上確定抗原以及抗原綁定的特定免疫受體的序列，為實(shí)現(xiàn)免疫建圖,、未來(lái)治療和通用診斷測(cè)試創(chuàng)造了機(jī)會(huì),。

很快，整合將不再局限于這種“只讀”性質(zhì)的多層組學(xué)整合分析,，而是會(huì)結(jié)合CRISPR（一種基因編輯技術(shù)）等強(qiáng)大的生物“寫入”功能,。通過(guò)將CRISPR與基因組工具以及單細(xì)胞分析相結(jié)合，科學(xué)家將能夠并行不悖地進(jìn)行寫入（編輯DNA）,、檢測(cè)（分析某些生物學(xué)輸出）,、讀取（測(cè)序）,，查詢多項(xiàng)讀數(shù)（DNA,、RNA、蛋白質(zhì),、表型）,。

Perturb-SEQ是這種整合的早期例子，利用Perturb-SEQ進(jìn)行多重分析時(shí),，會(huì)在單個(gè)細(xì)胞中擾動(dòng)數(shù)萬(wàn)個(gè)單個(gè)基因,，通過(guò)單細(xì)胞RNA分型來(lái)分析這種擾動(dòng)產(chǎn)生的表型結(jié)果，從而實(shí)現(xiàn)了全面分析基因功能的功能性基因組學(xué),。

這種整合的下一步是在單個(gè)細(xì)胞中10000個(gè)不同位置上進(jìn)行各不一樣的基因?qū)懭?，測(cè)試表型變化，其中包括單細(xì)胞RNA分型后基因表達(dá)的改變等,。

迄今為止,，生物寫入一直費(fèi)錢費(fèi)力，僅限于大規(guī)模項(xiàng)目,。然而,，正在開(kāi)發(fā)用于單細(xì)胞多重CRISPR編輯的桌面儀器，從而確保研究人員能夠整合單細(xì)胞DNA讀寫,。我們很快就能感受到在閉環(huán)系統(tǒng)中整合DNA讀寫的作用,，還將看到這種做法會(huì)大大提升學(xué)習(xí)速度。

這些新方法將推動(dòng)醫(yī)學(xué)領(lǐng)域取得激動(dòng)人心的重大進(jìn)步,，同時(shí)也突出強(qiáng)調(diào)了生物一直以來(lái)極度復(fù)雜的特性,。毫無(wú)疑問(wèn)，未來(lái)20年將出現(xiàn)的機(jī)遇和進(jìn)步將比這20年更令人興奮,。（財(cái)富中文網(wǎng)）

約翰·施蒂爾普納格爾是Illumina和Ariosa Diagnostics的聯(lián)合創(chuàng)始人,。此外，他目前擔(dān)任10X Genomics董事會(huì)主席,。布萊恩·羅伯茨是Venrock的合伙人,。

譯者：Agatha

Our founding thesis at Illumina was straightforward: biology is extremely complex and to unravel that biology, and thereby dramatically impact human medicine, would require a much larger scale of experimentation at an exponentially cheaper cost per experiment. And it worked, sort of.

For 20 years, Illumina has powered the genomics revolution through dramatic miniaturization and multiplexed assay development. First with arrays, and now with next-generation sequencing, experiments are conducted on the nanometer scale with billions of assays occurring in parallel. Those tools, and their applications, created the promise of personalized medicine and are revolutionizing entire areas of medical practice.

Non-invasive prenatal testing was introduced in late 2011, and only 6 years later more than 2 million tests are run annually. Sequencing of tumors, either via biopsy or through cell-free analysis, is changing cancer diagnosis and treatment. Enough so that 2018 saw the first initial drug approval for therapeutic usage based on tumor genetic signature (NTRK gene fusion) rather than the traditional delineation of cancer origin (lung, colon, etc).

Still however, two decades later, the promise of personalized medicine is primarily just that, a promise. Biology remains very complex. It seems that every time we unravel a portion of that complexity, we uncover more complexity.

As we start to understand a particular gene’s function, we then need to understand that gene’s networks, its post-translational modifications at the RNA and protein level, and its complex cellular regulation, before we can even get to the question we want to ask: how all of that impacts disease.

Reducing the sequencing cost for a whole human genome from more than $1 billion to under $1,000 has driven enormous progress on this very incomplete puzzle. Reducing it to $100 will generate additional discoveries; however, those discoveries will be incremental, not revolutionary. Scientists require orthogonal approaches and novel capabilities in the tools they use to catapult forward our knowledge base. We believe that two approaches fit this criteria: dramatic improvement in resolution, and the integration of previously disparate disciplines and capabilities.

A Resolution Revolution: Single Cell Genomics

Until very recently, genomics had relied on the analysis of bulk tissue samples. While important questions were answered, bulk analysis ignores critical biological information. Any biopsy is comprised of a variety of cell types.

More than 200 human cell types are known (and counting). Blending all of the cells of a sample together for bulk analysis obscures function at the cellular level and ignores proportional differences in cell types. With this type of analysis, we are only able to reveal the most obvious information. Single-cell genomics – the ability to analyze each cell individually on a genome-wide scale – is now providing the lens needed to match biological complexity.

10x Genomics (and others) are pioneering single-cell genomics approaches and expanding the types of analysis possible on the single cell level, including DNA sequencing, RNA profiling, epigenetic discovery, and immunology. More and more applications of single cell analysis will be developed now that this important platform has become robust and its usage is proliferating across the research ecosystem.

The cellular level insights from single-cell genomics are really just starting to demonstrate the power of this approach; for instance, the identification of a rare airway cell type (pulmonary ionocyte) now deemed to be important in cystic fibrosis.

That said, there is even another dimension of information to augment these experiments – how these cells are positioned and interact in the tissue. This spatial information will be vital to understanding some diseases.

Traditionally, the analysis of tissue has been the domain of pathology – thin slices stained with one or two markers. An important future area of tool development will bring genomic-level analysis to tissue with intact spatial positioning of cells, thereby allowing genomic assays to be run on cells at the tissue level.

The Power of Integration

While the idea of integrating DNA, RNA and protein data has been talked about for over a decade, this data has suffered from both a lack of resolution as well as unifying assay technology. However, recent advances are overcoming these limitations; it is now possible to analyze, at the single-cell level in the same sample, both RNA transcription and epigenetic changes, providing an insight into how epigenetics affects transcription.

Likewise, one can determine both the antigen and the sequence of the specific immune receptor to which the antigen binds with single-cell discrimination, opening up the opportunity for immune mapping, future therapeutics and universal diagnostic tests.

Soon, integration will expand beyond these multi-analyte biological “read only” assays, to incorporate powerful biological “writing” capabilities such as CRISPR. Integration of CRISPR with genomic tools and single-cell analysis will allow scientists to write (edit DNA), test (assay for some biological output), and read (sequence) in a parallel fashion, interrogating multiple readouts (DNA, RNA, protein, phenotype).

An early example of this integration is Perturb-SEQ, where, in a multiplexed assay, tens of thousands of individual genes are disrupted in single cells with the phenotypic results of that disruption being analyzed through single-cell RNA profiling, enabling comprehensive functional genomics.

The next step in this integration will be to write uniquely at 10,000s of different locations in single cells, then test for phenotypic change, including changes in gene expression through single-cell RNA profiling.

To date, biological writing has been an expensive and manually laborious process confined to large-scale efforts. However, desktop instruments are in development for single-cell, multiplexed CRISPR editing to enable researchers to integrate single-cell DNA reading and writing. We will soon appreciate the power of integrating DNA reading and writing in a closed loop system and the dramatically faster pace of learning that will result.

These novel approaches will drive crucial and exciting progress in medicine, while underscoring the continued enormity of the scale of biological complexity. The opportunities and advances of the next 20 years will undoubtedly be even more exciting than the last.

John Stuelpnagel is a co-founder of both Illumina and Ariosa Diagnostics. Additionally, he currently serves as Chairman of the Board of Directors of 10X Genomics. Bryan Roberts is a partner at Venrock.