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

20年來，Illumina通過大力發(fā)展小型化多重分析，為基因組學(xué)革命注入了動力。無論是一開始的陣列，還是現(xiàn)在的新一代基因測序，都在納米級別的實驗中并列進行了數(shù)十億次分析。這些工具及其應(yīng)用為實現(xiàn)精準(zhǔn)醫(yī)療帶來希望，正在徹底改變整個醫(yī)療領(lǐng)域的實踐。

2011年底推出了非侵入性產(chǎn)前檢測，僅僅過了六年，每年檢測數(shù)就高達(dá)200多萬次。通過活組織檢查或游離細(xì)胞分析進行腫瘤測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)我們解開了其中的一部分復(fù)雜性時，就會發(fā)現(xiàn)更大范圍的復(fù)雜性。

當(dāng)我們開始了解某一特定基因的功能時，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)機制，才有可能提出我們真正想問的問題：上述所有因素如何作用于疾病。

將整個人類基因組的測序成本從超過10億美元降至1000美元以下，推動我們在這個還遠(yuǎn)沒有答完的難題上取得了巨大進步。如果將成本降至100美元，會有更多發(fā)現(xiàn)；然而，這些發(fā)現(xiàn)將是增補性的而非革命性的。科學(xué)家在拓寬人類知識庫的過程中，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)獨立的學(xué)科和能力進行整合。

分辨率革命：單細(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)胞混在一起進行整體分析會導(dǎo)致不同細(xì)胞的功能被混為一談，也會忽略不同細(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)單獨分析每個細(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）正在開創(chuàng)單細(xì)胞基因組學(xué)的新方法，擴展可以在單細(xì)胞水平上進行的分析類型，包括DNA測序、RNA分析、表觀遺傳學(xué)和免疫學(xué)等。由于這類平臺意義重大且發(fā)展迅猛，在整個研究生態(tài)中的應(yīng)用也愈加廣泛，今后必將開發(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化實驗——這些細(xì)胞在組織中的位置和相互作用。這類空間信息對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——用有一兩個標(biāo)記的薄片。未來工具開發(fā)的一個重要領(lǐng)域?qū)⑹菍哂型暾?xì)胞空間定位的組織進行基因組層面的分析，從而在組織層面上對細(xì)胞進行基因組分析。

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。但是最近的一些進展正在突破這些局限；現(xiàn)在可以對同一樣本在單細(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，為實現(xiàn)免疫建圖、未來治療和通用診斷測試創(chuàng)造了機會。

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，而是會結(jié)合CRISPR（一種基因編輯技術(shù)）等強大的生物“寫入”功能。通過將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行寫入（編輯DNA）、檢測（分析某些生物學(xué)輸出）、讀取（測序），查詢多項讀數(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進行多重分析時，會在單個細(xì)胞中擾動數(shù)萬個單個基因，通過單細(xì)胞RNA分型來分析這種擾動產(chǎn)生的表型結(jié)果，從而實現(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細(xì)胞中10000個不同位置上進行各不一樣的基因?qū)懭耄瑴y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，生物寫入一直費錢費力，僅限于大規(guī)模項目。然而，正在開發(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大大提升學(xué)習(xí)速度。

這些新方法將推動醫(yī)學(xué)領(lǐng)域取得激動人心的重大進步，同時也突出強調(diào)了生物一直以來極度復(fù)雜的特性。毫無疑問，未來20年將出現(xiàn)的機遇和進步將比這20年更令人興奮。（財富中文網(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.