时空组学揭示肝细胞癌异质性的应用与展望

李汶欣 文文

引用本文: 李汶欣,文文. 时空组学揭示肝细胞癌异质性的应用与展望[J]. 海军军医大学学报,2025,46(9):1095-1102. DOI: 10.16781/j.CN31-2187/R.20240640..
Citation: LI W, WEN W. Application and prospect of spatiotemporal omics in revealing heterogeneity of hepatocellular carcinoma[J]. Acad J Naval Med Univ, 2025, 46(9): 1095-1102. DOI: 10.16781/j.CN31-2187/R.20240640..

时空组学揭示肝细胞癌异质性的应用与展望

doi: 10.16781/j.CN31-2187/R.20240640
基金项目: 

国家重点研发计划 2021YFF0700200.

详细信息
    作者简介:

    李汶欣,硕士生. E-mail: wenxinli2024@163.com;

    文文  研究员,博士生导师,海军军医大学第三附属医院实验诊断科主任,国家肝癌科学中心课题组长,教育部“长江学者”特聘教授,美国哥伦比亚大学访问学者。国家优秀青年科学基金获得者,先后入选国防科技卓越青年、军队高层次科技创新人才工程学科拔尖计划、上海市卫生健康学科带头人等。获国务院政府特殊津贴及军队优秀专业技术人员一类岗位津贴。兼任中国医药生物技术协会第四届生物诊断技术分会副主任委员、中国医师协会检验医师分会第五届委员会委员和德国癌症学会Journal of Cancer Research and Clinical Oncology杂志副主编。长期从事肝胆肿瘤发生和发展机制研究,主持国家、军队、省部级基金课题10余项,在CellCell DiscoveryHepatologyGut等SCI收录期刊发表论文30余篇,参编学术专著或译著5部,获国家发明专利授权5项、软件著作权2项.

    通讯作者:

    文文, E-mail: wenwen_smmu@163.com.

Application and prospect of spatiotemporal omics in revealing heterogeneity of hepatocellular carcinoma

Funds: 

National Key Research and Development Program of China 2021YFF0700200.

  • 摘要: 肝细胞癌是原发性肝癌中最常见的类型,具有高度异质性,其肿瘤细胞的基因表达差异、细胞亚群的异常分布及肿瘤微环境中细胞的复杂交互作用是导致部分患者免疫和靶向治疗失败的关键因素。理解肝细胞癌异质性对于揭示治疗耐药机制及开发新的治疗策略至关重要。时空组学技术可以在连续的时间和空间维度下解析组织细胞水平的分子表达图谱。近年来,以空间转录组和空间蛋白质组为代表的时空组学技术快速发展,两者在揭示组织表达谱特征的同时保留了空间位置信息,能确定分子在细胞或组织中的分布、定位和相互作用,为解析肝细胞癌肿瘤微环境异质性和治疗反应的差异性提供了新的研究视角。本文综述了时空组学技术在揭示肝细胞癌异质性包括细胞亚群、肿瘤微环境及治疗反应异质性方面的研究进展。

     

    Abstract: Hepatocellular carcinoma is the most common type of primary liver cancer, with high heterogeneity. The differential gene expression of tumor cells, abnormal distribution of cell subsets, and complex cell interactions in tumor microenvironment are crucial factors leading to the failure of immune and targeted therapies in some patients. Understanding the heterogeneity of hepatocellular carcinoma is of great importance when exploring the mechanism of drug resistance and developing new treatment strategies. Molecular expression profiles could be disclosed by spatiotemporal omics at the tissue and cellular levels across continuous time and space dimensions. Recently, spatial transcriptome and spatial proteomics, the representative of spatiotemporal omics technologies, have developed rapidly. They not only reveal the characteristics of tissue expression profiles while retaining spatial location information, but also uncover the distribution, localization and interaction of molecules in cells or tissues, providing a new perspective for analyzing the heterogeneity of tumor microenvironment and the difference of therapeutic responses in hepatocellular carcinoma. This article reviews the progress on spatiotemporal omics techniques in revealing the heterogeneity of hepatocellular carcinoma, including the heterogeneity of cell subsets, tumor microenvironment and therapeutic response.

     

  • 原发性肝癌是一种病因复杂且高度异质的恶性肿瘤,其中80%以上为肝细胞癌[1]。2020年全球癌症数据指出,肝细胞癌是全球发病率和死亡率最高的癌症之一,其高度异质性与治疗耐药和复发密切相关[2]。中国是世界上肝癌负担最重的国家之一,根据国家癌症中心的统计数据,2022年中国肝癌新发病例数和死亡病例数分别是367 700和316 500例[3]。肝细胞癌具有明显的肿瘤间和肿瘤内异质性,即不同患者间、同一患者的不同肿瘤组织以及同一肿瘤组织内细胞组成复杂且存在差异[4]。肿瘤细胞通过重编程获得多种分子改变以夺取生存优势,随着肿瘤进展,不同基因和分子改变的细胞亚克隆组成了异质性的肿瘤结节和肿瘤微环境。肝细胞癌与HBV、丙型肝炎病毒、非酒精性脂肪性肝炎(non-alcoholic steatohepatitis,NASH)、误食黄曲霉毒素等多种危险因素有关,由此产生的基因组不稳定、分子和信号转导网络紊乱及微环境差异导致肝细胞癌容易出现更高水平的肿瘤异质性[5]。同时肝细胞癌的高度异质性使治疗选择变得复杂,并与患者治疗无应答和较差的预后密切相关[6-7]

    由于肝细胞癌起病隐匿,大多数患者确诊时已经处于癌症晚期,只有约30%的患者有手术治疗机会[8]。晚期肝细胞癌患者的主要治疗手段是综合治疗,包括免疫治疗和靶向治疗。索拉非尼(sorafenib)是首个被批准用于晚期肝细胞癌治疗的靶向药物,将中位生存期从8个月延长至11个月[9],是目前晚期肝细胞癌患者的一线治疗药物。免疫检查点抑制剂程序性死亡蛋白1(programmed death 1,PD-1)/程序性死亡蛋白配体1(programmed death ligand 1,PD-L1)抗体阿替利珠单抗(atezolizumab)和血管内皮生长因子抑制剂贝伐珠单抗(bevacizumab)联合治疗方案与索拉非尼相比延长了患者的无进展生存期和总生存期[10],也成为晚期肝细胞癌患者的一线治疗方案。尽管有多种新的靶向和免疫治疗药物如仑伐替尼(lenvatinib)、纳武利尤单抗(nivolumab)、帕博利珠单抗(pembrolizumab)给晚期肝细胞癌患者带来治疗希望[11-12],但肝细胞癌患者的5年生存率仍然低于20%[13]。肝细胞癌异质性、免疫浸润改变与患者治疗耐药和总生存率下降有关[14-16],针对肝细胞癌患者的分子和免疫图景异质性,选择最佳的联合用药方式将可能改善患者预后[17]。研究表明,细胞游离DNA分析可用于检测不同肿瘤结节的细胞亚克隆,支持个体化治疗方案的优化[18-19]。因此解析肝细胞癌的异质性、探索肝细胞癌进展和治疗耐药的机制、寻找新的治疗靶点对改善患者预后尤为重要。

    近年来发展成熟的单细胞测序技术在单细胞水平解析肿瘤微环境、识别关键细胞亚群以及寻找新的治疗靶点等方面做出了重要贡献[20]。但单细胞测序通常会造成肿瘤细胞空间位置信息的丢失,限制了对肿瘤组织空间结构的研究。时空组学技术可在空间维度上检测组织细胞内的分子信息,弥补了单细胞测序的空间定位缺陷。时空组学技术主要包括空间转录组和空间蛋白质组,能够揭示多种生物分子的空间分布和细胞间相互作用依赖的通讯网络[21],是生命科学研究的前沿技术。2016年,瑞典卡罗林斯卡学院的研究人员提出了基于空间条形码的空间转录组技术,通过在组织切片上整合空间位置信息和转录组信息,弥补了单细胞转录组测序空间信息丢失的不足[22]。按照获取空间信息方式的不同,空间转录组技术主要包括显微分割、荧光原位杂交、原位测序及空间条形码技术[21]。空间蛋白质组技术利用荧光成像、质谱等方法,揭示蛋白质在组织或细胞的表达、定位和相互作用[23]。主要空间组学技术见表 1

    表  1  主要空间组学技术的特点及其局限性
    Table  1  Characteristics and limitations of main spatial omics technologies
    Category of methods Principle Representative method Omics type Characteristic Limitation
    Microdissection The cells are located and isolated under microscope using tools such as laser or microneedle, followed by RNA sequencing LCM, DSP, Geo-seq Transcriptome Analyzing transcriptomic data for specific cells and single-cell resolution Low throughput and possibility of RNA degradation during the sample processing
    FISH The spatial distribution of the target gene observed through specifically combination between nucleic acid probe and target sequence smFISH, RNAscope, seqFISH, smHCR Transcriptome Localizing specific genes and single-cell resolution Difficult to detect short transcripts, long imaging time, and low throughput
    ISS In situ reverse transcription, amplification, and sequencing STARmap, ISS, BARseq Transcriptome Single-cell resolution and multiplexed capability for complex samples Low sequencing efficiency
    Spatial barcoding Correlating the expression of multiple genes with cellular location using specific spatial barcodes 10X Visium, Seq-Scope Transcriptome Single-cell transcriptional and spatial information and high throughput Low detection efficiency and resolution
    Mass spectrometry-based spatial proteomics Combining flow cytometry with mass spectrometry, labeling antibodies via heavy metal isotopes instead of fluorescent groups and quantifying the isotope labels through mass spectrometry IMC, MIBI Proteome High signal-to-noise ratio High cost
    Spatial protein technology based on fluorescence imaging The localization of target protein based on specific combination between antibodies labeled with fluorescent dyes and the target protein CODEX, Cell DIVE, CycIF Proteome High resolution Limited throughput and low signal-to-noise ratio
    LCM: Laser capture microdissection; DSP: Digital spatial profiling; Geo-seq: Geographical position sequencing; FISH: Fluorescence in situ hybridization; smFISH: Single-molecule fluorescence in situ hybridization; RNAscope: RNA single-molecule cyclic hybridization technology; seqFISH: Sequential fluorescence in situ hybridization; smHCR: Single-molecule hybridization chain reaction; ISS: In situ sequencing; STARmap: Spatially-resolved transcript amplicon readout mapping; BARseq: Barcoded anatomy resolved by sequencing; 10X Visium: 10X Genomics Visium spatial gene expression solution; Seq-Scope: Sequential spatial omics sequencing platform; IMC: Imaging mass cytometry; MIBI: Multiplexed ion beam imaging; CODEX: Co-detection by indexing; Cell DIVE: Cell digital imaging versatile environment; CycIF: Cyclic immunofluorescence.

    通过结合空间转录组和空间蛋白质组技术,可以在转录和蛋白质水平上全面解析肿瘤微环境,解码细胞亚群的空间分布和信号转导关系。这种整合方法为深入理解肿瘤进展和耐药机制提供了新途径,对肝细胞癌的基础及临床研究具有重要意义。

    肝细胞癌的细胞亚群异质性显著,特定细胞亚群在肿瘤进展和耐药中发挥重要作用[24]。肿瘤相关成纤维细胞(cancer associated fibroblast,CAF)是肿瘤微环境中主要的基质细胞,既往研究定义了CAF的多个亚群,这些亚群通过分泌细胞因子、参与细胞间通讯、重塑细胞外基质等方式介导肿瘤发生、进展、免疫抑制等过程[25]。但CAF的空间分布与肝细胞癌发生、发展相关的功能改变仍未完全探明。Jing等[26]结合空间转录组和空间蛋白质组等多组学方法,解析了CAF的空间定位及其与其他细胞的相互作用,发现了一类与肝细胞癌肿瘤干性和不良预后密切相关的CAF亚群,命名为F5-CAF。该亚群高表达Ⅰ型胶原蛋白α2链、Ⅳ型胶原蛋白α1链、Ⅳ型胶原蛋白α2链、结缔组织生长因子和卵泡抑素样蛋白1等基因,F5-CAF主要分布于癌巢和癌周,通过配体-受体相互作用、与共定位的高干性肿瘤细胞相互作用支持肿瘤干细胞的存活,促进肝细胞癌进展。

    肝细胞癌术后复发是影响患者生存期的关键因素,但目前对其空间结构、分子改变与复发之间的关系了解有限。Yang等[27]分析了46例原发和复发肝细胞癌手术切除治疗患者的358 729个单细胞中33种蛋白质的原位表达情况,详细描绘了复发肝细胞癌的空间生态系统图景,并确定了肿瘤周围区域富集的PD-L1+CD10+树突状细胞与肿瘤复发密切相关。PD-L1+CD10+树突状细胞通过配体-受体结合的方式与调节性T细胞和耗竭T细胞相互作用,促进了免疫抑制和免疫逃逸的肿瘤微环境形成,为肝细胞癌的复发创造了条件。

    肿瘤干细胞在肿瘤的起始、进展、转移中起重要作用[28],既往研究已经确定了CD47、上皮细胞黏附分子、跨膜蛋白prominin-1(PROM1)等肿瘤干性标志物[29],但不同肿瘤干细胞亚群在肝细胞癌中的定位和功能尚不完全清楚。Wu等[30]构建了7例原发性肝癌(包括5例肝细胞癌)患者的高分辨率空间转录组图景,涉及从非肿瘤到肿瘤前沿和肿瘤区域。研究结果表明肿瘤干细胞分散于肿瘤组织中,从肿瘤前沿到肿瘤再到门静脉癌栓,CD47+肿瘤干细胞和PROM1+肿瘤干细胞的数量都逐渐增加,其上皮间质转化、缺氧、TNF-α和调亡途径信号通路上调,这些信号通路的激活使这2个肿瘤干细胞亚群在肿瘤微环境重塑和肿瘤转移中起重要作用。

    空间转录组和空间蛋白质组技术在解析稀少而关键的细胞亚群基因表达、空间定位及细胞间相互作用方面具有独特优势。随着相关技术的进步和对肝细胞癌复杂组织结构的深入理解,研究焦点已从单一细胞亚群扩展到整个肿瘤微环境,这为开发新的综合治疗策略提供了研究基础[4]

    复杂的肿瘤微环境是肝细胞癌高异质性的重要来源,与临床预后密切相关[31]。肿瘤微环境包括基质细胞、免疫细胞、血管、细胞外基质和多种信号分子等。这些细胞可以通过产生趋化因子、生长因子和基质降解酶等促进血管生成、基底膜破坏和改变免疫细胞浸润,从而促进肿瘤进展。时空组学将组织细胞的分子信息与空间位置信息相关联,时空组学技术的应用和进步为解析复杂的肿瘤微环境提供了新的视角[32]。Liu等[33]将空间转录组与单细胞RNA测序和多重免疫荧光相结合,在肝细胞癌中发现了一个位于肿瘤边缘的肿瘤免疫屏障结构。该肿瘤免疫屏障由分泌型磷蛋白1(secreted phosphoprotein 1,SPP1)+巨噬细胞和CAF构成。在机制方面,缺氧微环境促进巨噬细胞SPP1的表达,而SPP1+巨噬细胞与CAF相互作用可刺激细胞外基质重塑、促进免疫屏障结构的形成,从而限制免疫细胞浸润,降低PD-1抗体治疗的有效性。该研究在小鼠肝细胞癌模型中验证了阻断SPP1能有效增强肝细胞癌PD-1抗体的治疗效果。

    NASH已成为肝细胞癌的重要病因,且NASH相关肝细胞癌的发病率逐年上升。与病毒性肝炎相关的肝细胞癌不同,NASH相关肝细胞癌患者对免疫检查点抑制剂的反应较差。为阐明两者的肿瘤免疫微环境和治疗反应差异的联系,Li等[34]使用质谱流式成像技术构建了HBV、丙型肝炎病毒、NASH相关肝细胞癌及健康肝脏的空间转录组图谱。研究结果表明,NASH相关肝细胞癌中,主要免疫效应细胞(如CD4+ T细胞和CD8+ T细胞)在肿瘤邻近的正常组织聚集,而在肿瘤内部很少;免疫抑制细胞(如骨髓来源抑制细胞和肿瘤相关巨噬细胞)优先分布于肿瘤周围。肿瘤周围免疫抑制细胞与效应T细胞的相互作用促进了免疫抑制微环境的形成和癌细胞的免疫逃逸。

    Wang等[35]通过空间转录组测序探索了肝细胞癌肿瘤微环境的空间表达模式,结果显示C-C基序趋化因子配体15在肿瘤核心区富集,通过招募和极化M2型巨噬细胞促进免疫抑制微环境的形成,且与患者预后不良密切相关,提示其可作为潜在的预后生物标志物;相反,C-C基序趋化因子配体19和C-C基序趋化因子配体21富集于免疫细胞浸润区域,通过招募CD3+ T细胞和CD20+ B细胞抑制癌细胞生长,且与良好的预后相关。肿瘤微环境的异质性是影响治疗反应的重要因素[36-37],利用时空组学技术探索肝细胞癌的微环境变化有助于研究肝细胞癌进展和耐药的机制,并辅助寻找新的治疗靶点。

    除了对肿瘤进行静态异质性分析外,时空组学还可用于揭示肿瘤治疗反应的差异机制。晚期肝细胞癌的综合治疗策略已经从单一靶向治疗拓宽到免疫检查点抑制剂联合靶向治疗,但仅约30%的患者对治疗有反应。肝细胞癌肿瘤间和肿瘤内的高度异质性,加之多种可选治疗方案,使得临床评估和治疗选择更具挑战[17]。时空组学通过阐明异质肿瘤细胞的细胞间相互作用、基因表达水平、空间结构改变,解析治疗应答和无应答患者的病理生理差异机制和相关的细胞信号通路变化,以期为难治性肝细胞癌患者制定个体化的综合治疗措施,从而改善患者预后。

    Zhang等[38]分析了15例接受新辅助卡博替尼和纳武利尤单抗治疗的肝细胞癌样本,其中5例表现出明显的治疗应答反应。通过空间转录组和空间蛋白质组技术发现治疗应答患者的免疫效应细胞浸润丰富,免疫抑制巨噬细胞减少,且免疫细胞和肿瘤细胞间的相互作用增强,B细胞成熟调节因子配对盒蛋白5显著激活。此外,1例早期复发患者的空间转录组分析显示其具有免疫抑制特征的肿瘤区域,表现为肿瘤细胞与CAF的相互作用增强和肿瘤干细胞标志物表达上调。Wang等[39]结合单细胞转录组测序和空间转录组技术识别出与肝细胞癌免疫治疗效果密切相关的骨膜蛋白(periostin,POSTN)+ CAF亚群,并揭示了其在免疫治疗耐药中的作用机制。该研究结果显示,POSTN+ CAF通过IL-6/STAT3信号通路促进巨噬细胞SPP1的表达,而POSTN+ CAF和SPP1+巨噬细胞在肿瘤区域的共定位和相互作用限制了效应T细胞的浸润,促进了免疫抑制肿瘤微环境的形成。

    Yang等[40]结合单细胞转录组和空间转录组测序发现丝氨酸肽酶抑制因子Kazal 1型(serine peptidase inhibitor Kazal type 1,SPINK1)的高表达与肝细胞癌治疗耐药和不良预后密切相关。在肿瘤细胞中,SPINK1与耐药相关的药物代谢基因羧酸酯酶2和细胞色素P450家族3亚家族A成员5共表达,导致肿瘤细胞对索拉非尼和奥沙利铂的耐药性增加,提示SPINK1可能是难治性肝细胞癌的潜在治疗靶点。

    Li等[41]用10X Genomics技术对3例肝细胞癌PD-1抗体治疗应答患者的肿瘤邻近组织进行空间转录组测序,并分析了从公共数据库获得的3例肝细胞癌PD-1抗体治疗无应答患者的数据。在应答和无应答患者的空间转录组图谱中均发现了阻碍免疫细胞浸润的“免疫屏障”的存在,该屏障由髓系细胞触发受体2(triggering receptor expressed on myeloid cells 2,TREM2)+巨噬细胞和POSTN+CAF构成,TREM2+巨噬细胞是主要的免疫抑制细胞,与T细胞耗竭显著相关。同时应答患者的CD8+ T细胞浸润明显高于无应答患者。以上结果提示患者对免疫治疗的应答受多种因素影响,包括免疫细胞的浸润数量及空间结构等。

    肝细胞癌的高度异质性是其治疗耐药和预后不良的主要因素,时空组学以高空间分辨率解码肝细胞癌的细胞和分子变化,成为肝细胞癌异质性研究的关键工具,基于时空组学的肝细胞癌异质性的重要研究成果见表 2图 1。然而,目前的时空组学技术仍处于初级发展阶段,存在一定局限性,如分辨率相对于单细胞测序较低,空间转录组和空间蛋白质组都需要圈定组织中的多个细胞(RNA检测需要不少于50个细胞、蛋白质检测需要至少10个细胞)、暂时还无法实现单细胞水平上的检测等[42-43]。时空组学技术样本处理过程复杂,对检测和分析的仪器要求较高,所产生的文件格式和数据结构多样,数据量巨大,通常达到数太字节(terabyte,TB),访问和跨数据集融合分析都面临巨大的算法挑战,需要开发新的计算工具和算法以适应研究需要[44]。时空组学的灵敏度和准确性仍需提升,例如,空间转录组难以捕获低表达水平的RNA分子,这可能导致关键基因信息丢失[45]。在将图像数据转换为细胞数据的过程中,由于细胞重叠、形态不规则等因素,RNA或其他分子可能无法被正确匹配到对应细胞,进而影响后续功能分析的准确性和可靠性。

    表  2  基于时空组学解析肝细胞癌异质性的重要研究成果
    Table  2  Key findings on heterogeneity of hepatocellular carcinoma based on spatiotemporal omics
    Author Technology Omics type Key finding
    Jing, et al[26] Mass spectrometry-based spatial proteomics, 10X Visium Spatial transcriptome, spatial proteome Definition of CAF subgroups that promote hepatocellular carcinoma progression
    Yang, et al[27] IMC Spatial proteome Profiling the multicellular spatial structure of primary and recurrent hepatocellular carcinomas, and the interaction between PD-L1+CD103+ dendritic cells, regulatory T cells and exhausted T cells aggravating immunosuppression and immune escape, which is an important driver of hepatocellular carcinoma recurrence
    Wu, et al[30] 10X Visium Spatial transcriptome Defining PROM1 and CD47 as potential targets for preventing tumor vascular metastasis and profiling a tertiary lymphoid structure in primary hepatocellular carcinoma
    Li, et al[34] IMC Spatial proteome Profiling single-cell spatial mapping of patients within NASH-associated hepatocellular carcinoma, and confirming that interactions between myeloid-derived suppressor cells, tumor-associated macrophages and effector T cells underlying immunosuppression in NASH-associated hepatocellular carcinoma
    Wang, et al[35] 10X Visium Spatial transcriptome Revealing spatial expression patterns in the immune microenvironment of hepatocellular carcinoma, and emphasizing the importance of key molecules such as C-C motif chemokine ligands 15, 19, and 21 for patient prognostic assessment
    Wang, et al[39] 10X Visium Spatial transcriptome Emphasizing the critical role of POSTN+ CAF in the immune response barrier in hepatocellular carcinoma and the potential benefit of improving immunotherapy response by targeting this subpopulation
    Yang, et al[40] 10X Visium Spatial transcriptome Revealing the significant role of SPINK1 in predicting hepatocellular carcinoma drug resistance and identifying it as a potential therapeutic target for refractory hepatocellular carcinoma
    Li, et al[41] 10X Visium Spatial transcriptome Differences in the immune microenvironment of hepatocellular carcinoma patients partially explain the differential response to anti-PD-1 therapy, and targeting TREM2+ macrophages potentially enhances immunotherapy in hepatocellular carcinoma patients
    10X Visium: 10X Genomics Visium spatial gene expression solution; CAF: Cancer associated fibroblast; IMC: Imaging mass cytometry; PD-L1: Programmed death ligand 1; PROM1: Prominin-1; NASH: Non-alcoholic steatohepatitis; POSTN: Periostin; SPINK1: Serine peptidase inhibitor Kazal type 1; PD-1: Programmed death 1; TREM2: Triggering receptor expressed on myeloid cells 2.
    图  1  时空组学在肝细胞癌异质性研究中的成果
    Fig.  1  Achievements of spatiotemporal omics in heterogeneity of hepatocellular carcinoma
    下载: 全尺寸图片

    以空间转录组和空间蛋白质组为代表的时空组学技术正处在高速发展时期,其在肿瘤研究中的应用前景正被广泛认可[46]。随着新型探针、测序策略、抗体、成像系统的问世,以及算法的改进和商业化时空组学平台的发展,时空组学的空间分辨率、灵敏度和准确性有望进一步提升,使用成本将会降低,这将促进其在肿瘤异质性研究中更广泛的应用。此外,可以预见的发展方向是将空间基因组、空间转录组和空间蛋白质组等多组学联合应用与整合分析[21],能全面揭示肿瘤发生、发展过程中细胞空间动态变化的图景,描绘异质性的肿瘤微环境特征,这将为肿瘤患者的早期诊断和个体化治疗决策提供重要参考,推动精准医学的发展。目前,大多数空间组学数据来自二维的薄组织切片,通过计算方法可以重构三维结构,但这一过程非常耗时。三维空间组学能够更真实地构建完整的肿瘤生态图谱并解析完整的组织结构,对推动肿瘤免疫微环境研究及提高药物筛选和疗法开发效率具有重要意义,未来的研究将着力开发更高效和经济的三维空间组学技术,使其成为研究肿瘤生物学和推动新药研发的有力工具[21]

  • 图  1   时空组学在肝细胞癌异质性研究中的成果

    Fig.  1   Achievements of spatiotemporal omics in heterogeneity of hepatocellular carcinoma

    下载: 全尺寸图片

    表  1   主要空间组学技术的特点及其局限性

    Table  1   Characteristics and limitations of main spatial omics technologies

    Category of methods Principle Representative method Omics type Characteristic Limitation
    Microdissection The cells are located and isolated under microscope using tools such as laser or microneedle, followed by RNA sequencing LCM, DSP, Geo-seq Transcriptome Analyzing transcriptomic data for specific cells and single-cell resolution Low throughput and possibility of RNA degradation during the sample processing
    FISH The spatial distribution of the target gene observed through specifically combination between nucleic acid probe and target sequence smFISH, RNAscope, seqFISH, smHCR Transcriptome Localizing specific genes and single-cell resolution Difficult to detect short transcripts, long imaging time, and low throughput
    ISS In situ reverse transcription, amplification, and sequencing STARmap, ISS, BARseq Transcriptome Single-cell resolution and multiplexed capability for complex samples Low sequencing efficiency
    Spatial barcoding Correlating the expression of multiple genes with cellular location using specific spatial barcodes 10X Visium, Seq-Scope Transcriptome Single-cell transcriptional and spatial information and high throughput Low detection efficiency and resolution
    Mass spectrometry-based spatial proteomics Combining flow cytometry with mass spectrometry, labeling antibodies via heavy metal isotopes instead of fluorescent groups and quantifying the isotope labels through mass spectrometry IMC, MIBI Proteome High signal-to-noise ratio High cost
    Spatial protein technology based on fluorescence imaging The localization of target protein based on specific combination between antibodies labeled with fluorescent dyes and the target protein CODEX, Cell DIVE, CycIF Proteome High resolution Limited throughput and low signal-to-noise ratio
    LCM: Laser capture microdissection; DSP: Digital spatial profiling; Geo-seq: Geographical position sequencing; FISH: Fluorescence in situ hybridization; smFISH: Single-molecule fluorescence in situ hybridization; RNAscope: RNA single-molecule cyclic hybridization technology; seqFISH: Sequential fluorescence in situ hybridization; smHCR: Single-molecule hybridization chain reaction; ISS: In situ sequencing; STARmap: Spatially-resolved transcript amplicon readout mapping; BARseq: Barcoded anatomy resolved by sequencing; 10X Visium: 10X Genomics Visium spatial gene expression solution; Seq-Scope: Sequential spatial omics sequencing platform; IMC: Imaging mass cytometry; MIBI: Multiplexed ion beam imaging; CODEX: Co-detection by indexing; Cell DIVE: Cell digital imaging versatile environment; CycIF: Cyclic immunofluorescence.

    表  2   基于时空组学解析肝细胞癌异质性的重要研究成果

    Table  2   Key findings on heterogeneity of hepatocellular carcinoma based on spatiotemporal omics

    Author Technology Omics type Key finding
    Jing, et al[26] Mass spectrometry-based spatial proteomics, 10X Visium Spatial transcriptome, spatial proteome Definition of CAF subgroups that promote hepatocellular carcinoma progression
    Yang, et al[27] IMC Spatial proteome Profiling the multicellular spatial structure of primary and recurrent hepatocellular carcinomas, and the interaction between PD-L1+CD103+ dendritic cells, regulatory T cells and exhausted T cells aggravating immunosuppression and immune escape, which is an important driver of hepatocellular carcinoma recurrence
    Wu, et al[30] 10X Visium Spatial transcriptome Defining PROM1 and CD47 as potential targets for preventing tumor vascular metastasis and profiling a tertiary lymphoid structure in primary hepatocellular carcinoma
    Li, et al[34] IMC Spatial proteome Profiling single-cell spatial mapping of patients within NASH-associated hepatocellular carcinoma, and confirming that interactions between myeloid-derived suppressor cells, tumor-associated macrophages and effector T cells underlying immunosuppression in NASH-associated hepatocellular carcinoma
    Wang, et al[35] 10X Visium Spatial transcriptome Revealing spatial expression patterns in the immune microenvironment of hepatocellular carcinoma, and emphasizing the importance of key molecules such as C-C motif chemokine ligands 15, 19, and 21 for patient prognostic assessment
    Wang, et al[39] 10X Visium Spatial transcriptome Emphasizing the critical role of POSTN+ CAF in the immune response barrier in hepatocellular carcinoma and the potential benefit of improving immunotherapy response by targeting this subpopulation
    Yang, et al[40] 10X Visium Spatial transcriptome Revealing the significant role of SPINK1 in predicting hepatocellular carcinoma drug resistance and identifying it as a potential therapeutic target for refractory hepatocellular carcinoma
    Li, et al[41] 10X Visium Spatial transcriptome Differences in the immune microenvironment of hepatocellular carcinoma patients partially explain the differential response to anti-PD-1 therapy, and targeting TREM2+ macrophages potentially enhances immunotherapy in hepatocellular carcinoma patients
    10X Visium: 10X Genomics Visium spatial gene expression solution; CAF: Cancer associated fibroblast; IMC: Imaging mass cytometry; PD-L1: Programmed death ligand 1; PROM1: Prominin-1; NASH: Non-alcoholic steatohepatitis; POSTN: Periostin; SPINK1: Serine peptidase inhibitor Kazal type 1; PD-1: Programmed death 1; TREM2: Triggering receptor expressed on myeloid cells 2.
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  • 收稿日期:  2024-09-11
  • 修回日期:  2024-12-26

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