Tumor tissue is a multifaceted ecosystem in which tumors cells are surrounded and influenced by a myriad of non‐cancerous cells including immune, stromal, vascular, and other cell types.^[1^] Driven by stochastic genetic mutations, epigenetic modifications, and aberrant gene expression profiles, tumor cells themselves also exhibit extraordinary intratumoral heterogeneity that gives rise to malfunctioning of signaling networks and plays important roles in tumor invasion, proliferation, metastasis, as well as stromal remodeling and immune system suppression.^[2, 3^] This pronounced cell‐to‐cell variations make traditional bulk‐level profiling far away from an accurate representation of the tumor ecosystem. In this regard, single‐cell multi‐omics tools provide a great opportunity for researchers to improve the understanding of molecular roles of tumor heterogeneity, thanks to their high spatiotemporal resolutions down to the level of single cells as well as their analytical capacity at the systems scale.^[4-6^]

To date, a panoply of mono‐omics technologies have been developed to effectively profile different molecular layers of single cells including genome, epigenome, transcriptome, proteome, metabolome, and so forth.^[7-9^] The quantification of these molecular signatures of cellular processes at single‐cell resolution enables us to ask questions from perspectives previously unattainable and thereby facilitates our understanding of the cause and consequence of tumor heterogeneity in tumorigenesis, metastasis, and immune response. In addition, building on the development of these mono‐omics technologies, tools for simultaneous measurement of multiple omic layers from the same single cells have emerged in recent years through the rational design of bio‐recognition interface and the leverage of advanced biotechnologies.^[10-13^] Single‐cell multi‐omics tools allow for interrogating the links between different classes of biomolecules to resolve the interplays between distinct molecular landscapes. These integrated measurements not only offer a holistic view of cellular compositions and phenotypic states of a given population, but also enable detailed investigations into the developmental history, inter‐ and intracellular signal transduction, as well as the roles of significant subpopulations or rare cell types in specific physiological or pathological processes across multiple modalities. In the meantime, such measurements pose new challenges in data analysis and interpretation, as different omic layers require different suites of analytical approaches that are not always compatible. While each single cell can provide an anchor to connect different data modalities, computational frameworks that can integrate diverse sets of information from different molecular layers into harmonized atlases for effective visualization, hypothesis generation, and data interpretation are still a pressing need in the field.^[14^]

In this special issue, we have collected current efforts that have been made in the development of novel single‐cell technologies as well as their applications in immuno‐oncology and cancer systems biology. Liu et al. reported a multiplexed single‐cell analytical platform to quantify secreted cytokines from single cells.^[15^] Secreted cytokines play important roles in mediating cell‐cell communications in various physiological and pathological processes. Conventional single‐cell cytokine secretion assays are mainly based on the adaption of an enzyme‐linked immunosorbent assay which measures single‐cell secretion footprint of a given cytokine through transforming the bio‐recognitions between the antibodies and cytokines into colorimetric or fluorescence signal readouts. Nevertheless, they are normally limited to three less than five cytokines that can be simultaneously detected due to the fluorescence spectral overlap. To improve the assay multiplexity without the adaption of sophisticated experiment handling or bulky equipment, a high‐density polydimethylsiloxane microwell stencil was sandwiched between two antibody‐coated glass slides to form periodic compartments for single‐cell trapping, culturing, and cytokine secretion profiling. With 5‐plexed and 3‐fluorescence colors designed for detection antibodies, five or more secreted cytokines from more than 1000 single cells could be simultaneously profiled without the adaption of sophisticated fluid handling system or bulky equipment. Moreover, the authors demonstrated the utility of this single‐cell technology through an investigation of secretome heterogeneity of human monocytic U937 cells in response to lipopolysaccharide and phorbol myristate acetate stimulations. The technical simplicity and high throughput make this single‐cell secretion assay a unique and informative tool in dissecting cellular heterogeneity in secretome signatures.

As an attempt to move forward from basic research to translational and clinical applications, Bowman et al. adapted a highly‐multiplexed single‐cell proteomic assay (32‐plex, IsoLight automation system) to characterize the polyfunctionality of pre‐infusion anti‐CD19 chimeric antigen receptor (CAR)‐T cell products from a cohort of patients with Non‐Hodgkin's Lymphoma via quantitatively profiling 30+ cytokines secreted from CD4+ and CD8+ T cells in response to CD19 antigen‐specific stimulation.^[16^] To better resolve the clinical relevance of the CAR‐T cell polyfunctionality, a comprehensive visualization toolkit was developed by integrating 3D Uniform Manifold Approximation and Projection and t‐distributed stochastic neighbor embedding into a proteomic analysis pipeline which could be further built into the IsoLight system. The combined use of commercial single‐cell proteomic assays and the new bioinformatics pipeline developed in this work was envisaged to promote the understanding of underlying mechanisms in cell‐based immunotherapies for improved personalized cancer medicine.

Single‐cell RNA‐sequencing (scRNA‐seq) has become one of the most widely used single‐cell analytical approaches due to its high‐throughput, robust performance, and good compatibility to be coupled with other single‐cell profiling technologies. Dong et al. recently incorporated scRNA‐seq with a rare cell enrichment approach to decipher the intratumoral heterogeneity of disseminated tumor cells (DTCs) derived from liquid biopsy samples.^[17^] DTCs are tumor cells spreading from primary sites to body fluids, which are considered as an important biomarker for prognostic evaluation of cancer patients because of their critical roles in cancer metastasis. However, the rarity and low viability of DTCs, as well as the co‐existence of large amounts of non‐cancerous cells, imposes great challenges in transcriptomic profiling of DTCs through scRNA‐seq technology. To overcome this obstacle, a CD45 depletion kit was employed to remove the leukocytes in liquid biopsies sampled from malignant pleural effusions (MPE) and resultant cell samples were subsequently processed for scRNA‐seq. Five main cell populations including tumor, mesothelial, monocyte, T, and B cells were identified while the DTCs could be further clustered into four subgroups with their distinct functional features characterized. These results demonstrated that the rational combination of rare cell enrichment methods with scRNA‐seq technology paved a new avenue to molecular profiling of rare cell types.

Most of the single‐cell multi‐omics tools mainly focus on the measurements of cellular biomolecules which only contain the chemical essence of cellular functions, but overlook important cellular physical information (e.g., cell mass, size, and motility). Recently, Han et al. developed a microfluidic cell trap array that can monitor the motility behavior of single hematopoietic stem/progenitor cells (HSPCs)—a clinically relevant parameter for peripheral blood stem cell transplantation.^[18^] Following on‐chip sorting and selection, collected cells were subjected to RNA‐seq to build the links between HSPC motility and stem‐cell maintenance. This approach not only offers a novel strategy to decipher motility heterogeneity in HSPCs but facilitates the screening of HSPC mobilization compounds as well.

Meanwhile, several reviews in this special issue attempted to portray the field of single‐cell multi‐omics from different perspectives. Yang et al. comprehensively summarized the principles, developments, advantages, and limitations of recently emerged single‐cell proteomic technologies, along with their applications in dissecting cellular heterogeneity.^[19^] In parallel, Kravchenko‐Balasha highlighted the implications of bulk‐ and single‐cell proteomic assays as well as associated computational tools in unveiling and constructing the inter‐ and intratumoral signaling networks for personalized medicine.^[20^] Zhu et al. focused on the elaboration of scRNA‐seq and associated technologies adapted to the studies of hematological diseases, revealing the history of single‐cell omics from basic research to translational and clinical applications.^[21^] Peng et al. comprehensively reviewed recently developed single‐cell multi‐omics tools with detailed comparisons of their properties from different perspectives. Meanwhile, their applications in tumor‐immune interactions were also highlighted.^[22^]

Single‐cell multi‐omics is a rapidly growing field that calls for cross‐disciplinary efforts ranging from chemistry, physics, biology, engineering, computational science to medicine. We greatly appreciate all contributors to this special issue and their achievements in this exciting field. We encourage more scientists to join us to leverage these powerful single‐cell toolkits to gain a systems‐level understanding of cellular states, interactions, and behavior in human cancers and transform the development of diagnostic and therapeutic approaches in oncology.

肿瘤组织是一个多方面的生态系统，其中肿瘤细胞被无数非癌细胞（包括免疫、基质、血管和其他细胞类型）包围和影响。^[ 1 ^]在随机基因突变、表观遗传修饰和异常基因表达谱的驱动下，肿瘤细胞本身也表现出非凡的瘤内异质性，导致信号网络故障，并在肿瘤侵袭、增殖、转移以及基质重塑和免疫系统抑制。^[ 2, 3 ^]这种明显的细胞间差异使传统的批量分析远离肿瘤生态系统的准确表示。在这方面，单细胞多组学工具为研究人员提供了一个很好的机会来提高对肿瘤异质性分子作用的理解，这要归功于它们在单细胞水平上的高时空分辨率以及它们在系统中的分析能力规模。^[ 4-6 ^]

迄今为止，已经开发出一整套单体组学技术来有效地分析单细胞的不同分子层，包括基因组、表观基因组、转录组、蛋白质组、代谢组等。^[ 7-9 ^]在单细胞分辨率下对细胞过程的这些分子特征进行量化，使我们能够从以前无法实现的角度提出问题，从而促进我们了解肿瘤发生、转移和免疫反应中肿瘤异质性的原因和后果。此外，在这些单体组学技术的发展基础上，通过合理设计生物识别界面和利用先进生物技术，近年来出现了同时测量来自同一单细胞的多个组学层的工具。^[ 10-13 ^]单细胞多组学工具允许询问不同类别生物分子之间的联系，以解决不同分子景观之间的相互作用。这些综合测量不仅提供了给定群体的细胞组成和表型状态的整体视图，而且能够详细研究发育历史、细胞间和细胞内信号转导，以及重要亚群或稀有细胞类型在跨多种模式的特定生理或病理过程。与此同时，此类测量对数据分析和解释提出了新的挑战，因为不同的组学层需要不同的分析方法套件，而这些方法并不总是兼容的。虽然每个单元格都可以提供一个锚点来连接不同的数据模式，^[ 14 ^]

在本期特刊中，我们收集了目前在开发新型单细胞技术及其在免疫肿瘤学和癌症系统生物学中的应用方面所做的努力。刘等人。报道了一个多路复用单细胞分析平台，用于量化单细胞分泌的细胞因子。^[ 15 ^]分泌的细胞因子在介导各种生理和病理过程中的细胞间通讯中发挥重要作用。传统的单细胞细胞因子分泌测定主要基于酶联免疫吸附测定的适应，该测定通过将抗体和细胞因子之间的生物识别转化为比色或荧光信号读数来测量给定细胞因子的单细胞分泌足迹。然而，由于荧光光谱重叠，它们通常限于可以同时检测到的三个少于五个的细胞因子。为了在不采用复杂的实验处理或笨重的设备的情况下提高检测的多重性，高密度聚二甲基硅氧烷微孔模板夹在两个抗体包被的载玻片之间，形成周期性的隔室，用于单细胞捕获、培养和细胞因子分泌分析。使用专为检测抗体设计的 5 重和 3 荧光颜色，可以同时分析来自 1000 多个单细胞的五种或更多分泌细胞因子，而无需采用复杂的流体处理系统或笨重的设备。此外，作者通过研究人类单核细胞 U937 细胞对脂多糖和佛波醇肉豆蔻酸酯醋酸盐刺激的分泌异质性，证明了这种单细胞技术的实用性。

为了从基础研究向前推进到转化和临床应用，Bowman 等人。采用高度多重的单细胞蛋白质组学分析（32 重，IsoLight 自动化系统）来表征来自一组非霍奇金淋巴瘤患者的输注前抗 CD19 嵌合抗原受体 (CAR)-T 细胞产物的多功能性通过定量分析 CD4+ 和 CD8+ T 细胞分泌的 30+ 细胞因子以响应 CD19 抗原特异性刺激。^[ 16 ^]为了更好地解决 CAR-T 细胞多功能性的临床相关性，通过将 3D Uniform Manifold Approximation and Projection 和 t 分布随机邻域嵌入集成到蛋白质组学分析管道中，开发了一个综合可视化工具包，该管道可以进一步构建到 IsoLight 系统中。商业单细胞蛋白质组学分析和这项工作中开发的新生物信息学管道的结合使用旨在促进对基于细胞的免疫疗法的潜在机制的理解，以改进个性化癌症医学。

单细胞RNA测序（scRNA-seq）由于其高通量、稳健的性能以及与其他单细胞分析技术的良好兼容性，已成为应用最广泛的单细胞分析方法之一。董等人。最近将 scRNA-seq 与一种罕见的细胞富集方法结合起来，以破译来自液体活检样本的播散性肿瘤细胞 (DTC) 的肿瘤内异质性。^[ 17 ^]DTCs是从原发部位扩散到体液的肿瘤细胞，由于它们在癌症转移中的关键作用，被认为是癌症患者预后评估的重要生物标志物。然而，DTCs 的稀有性和低活力，以及大量非癌细胞的共存，对通过 scRNA-seq 技术对 DTCs 的转录组学分析提出了巨大挑战。为了克服这一障碍，使用 CD45 去除试剂盒去除从恶性胸腔积液 (MPE) 中取样的液体活检中的白细胞，随后对所得细胞样品进行 scRNA-seq 处理。鉴定了五种主要细胞群，包括肿瘤、间皮细胞、单核细胞、T 和 B 细胞，而 DTC 可以进一步分为四个亚组，并具有不同的功能特征。

大多数单细胞多组学工具主要侧重于仅包含细胞功能化学本质的细胞生物分子的测量，而忽略了重要的细胞物理信息（例如，细胞质量、大小和运动性）。最近，韩等人。开发了一种微流控细胞陷阱阵列，可以监测单个造血干/祖细胞 (HSPC) 的运动行为，这是外周血干细胞移植的临床相关参数。^[ 18 ^]在芯片上分选和选择之后，对收集的细胞进行 RNA-seq，以建立 HSPC 运动性和干细胞维持之间的联系。这种方法不仅提供了一种新的策略来破译 HSPC 中的运动异质性，而且还有助于筛选 HSPC 动员化合物。

同时，本期特刊中的几篇评论试图从不同的角度描绘单细胞多组学领域。杨等人。全面总结了最近出现的单细胞蛋白质组学技术的原理、发展、优势​​和局限性，以及它们在剖析细胞异质性方面的应用。^[ 19 ^]与此同时，Kravchenko-Balasha 强调了大块细胞和单细胞蛋白质组学分析以及相关计算工具在揭示和构建用于个性化医疗的瘤间和瘤内信号网络方面的意义。^[ 20 ^]朱等人。专注于阐述适用于血液病研究的 scRNA-seq 和相关技术，揭示单细胞组学从基础研究到转化和临床应用的历史。^[ 21 ^]彭等。全面回顾了最近开发的单细胞多组学工具，并从不同角度详细比较了它们的特性。同时，它们在肿瘤免疫相互作用中的应用也得到了强调。^[ 22 ^]

单细胞多组学是一个快速发展的领域，需要从化学、物理学、生物学、工程学、计算科学到医学的跨学科努力。我们非常感谢本期特刊的所有贡献者以及他们在这个激动人心的领域取得的成就。我们鼓励更多的科学家加入我们，利用这些强大的单细胞工具包，从系统层面了解人类癌症中的细胞状态、相互作用和行为，并改变肿瘤学诊断和治疗方法的发展。