Single-cell transcriptome sequencing is a technology that analyzes gene expression profiles at the single-cell level through high-throughput sequencing. It overcomes the limitations of traditional bulk sequencing. After tissue dissociation, a single sequencing run can capture 500 to 20,000 cells. Libraries are constructed and sequenced for each individual cell, allowing the acquisition of gene structures and gene expression states per cell, identification of cell types, and comparison of cell type composition and abundance across different samples, thereby reflecting cellular heterogeneity.

Dynamic Microfluidics

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1.Single Cell Sequencing Technology：The company possesses industry-recognized platforms including 10x Genomics, BD Rhapsody, and NovelBio self-developed single-cell platforms, enabling high-quality and efficient single-cell sequencing.

2.Diverse Sample Processing Experience：The company has extensive experience in preparing single-cell suspensions, with experience spanning 50+ tissue types, 100+ cell types, and processing over 10,000 samples. This ensures cell viability meets sequencing requirements, resulting in reliable single-cell data.

3.Efficient Library Construction Technology：The company has mature single-cell sequencing library construction technology and automated library preparation platforms, capable of constructing libraries for 1,000 to 10,000 cells in a single batch.

4.Comprehensive Quality Control Process and Data Analysis Experience：NovelBio self-developed CytoNavigator, a high-throughput data analysis system, ensures data reliability and quality. Additionally, it provides personalized, customized data analysis services to help researchers extract meaningful information from massive single-cell datasets.

Sample Types:
• Tissue, blood, cultured cell lines, prepared single-cell suspensions.
Note: If the client provides tissue samples and lacks the capability to perform tissue dissociation to obtain single-cell suspensions, NovelBio will strive to provide technical and experimental assistance. However, due to the specificity of different sample types, we cannot guarantee the experimental method's applicability to all tissue types.

Quality Requirements:
• Cell viability greater than 70%
• Concentration of 500 - 2000 cells/μl
• Volume not less than 200μl
• Cell culture media and buffers must not contain Ca2+ and Mg2+
• Cell volume less than 40μm

Experimental Groups:
Batch1: 13 prostate cancer tumor tissue samples (12 primary, 1 lymph node metastasis);
(10x Genomics platform)
Batch2: 1 sample each of primary prostate cancer, tumor micro-metastasis lymph node, and normal lymph node;
(BD Rhapsody platform)
Batch3: 2 normal prostate, 2 indolent prostate cancer, 2 aggressive prostate cancer, 5 CRPC resistant tumor samples
(BD Rhapsody platform)

Capture Platforms:
10x Genomics & BD Rhapsody

Main Technical Methods:
scRNA-seq, Flow Cytometry, RT-qPCR, RNA-FISH, Immunofluorescence, Western Blot, RNA-seq, etc.

Analysis Methods:
Cell clustering analysis, subCluster analysis, differential gene expression analysis, GO/Pathway analysis, Cellphone analysis, QuSAGE analysis, Pseudotime analysis, etc.

Prostate cancer exhibits significant clinical heterogeneity, particularly reflected in spatial and clonal genomic diversity, but studies related to its transcriptomic heterogeneity are relatively limited. This study performed transcriptomic analysis on 36,424 single cells from 13 prostate tumors, identifying epithelial cells contributing to disease aggressiveness and observing the activation of multiple transcriptomic programs in the tumor microenvironment (TME) associated with disease progression. Concurrently, it found widespread KLK3 expression and that cancer cells can alter the T cell transcriptome. Further discovery indicated that ectopic KLK3 expression is associated with micro-metastasis. Through analysis of close intercellular communication, a group of endothelial cells capable of active communication with tumor cells (namely activated endothelial cells, aECs) was identified. Sequencing revealed that aECs are enriched in castration-resistant prostate cancer (CRPC) and can promote cancer cell invasion. The study also developed a user-friendly web interface to facilitate exploration of these sequencing data.

Using copy number variation analysis and correlation analysis of tumor cell marker genes/signaling pathways, epithelial cells were identified as luminal types, cell-cycle, and basal/intermediate types. We primarily investigated the relationship between these cell types and clinical prognosis. A basal/int. subpopulation specifically expressing the cytokine CCL2 was found to be associated with a better prognosis in prostate cancer, whereas the cell-cycle subpopulation was negatively correlated with prostate cancer prognosis.

In the study of T cells, we surprisingly discovered that T cells actually expressed the prostate cancer-specific gene KLK3. In-depth analysis of various public single-cell databases revealed a widespread characteristic of T cells expressing corresponding tumor marker genes.

Subsequently, focusing on KLK3, gene-subgroup functional correlation analysis and gene co-expression network analysis were performed. This revealed that pathways related to extracellular vesicles (EVs) and exosomal PSA were activated in association with KLK3. This suggested that tumors might transfer KLK3 to T cells via secreted EVs, reducing their cytotoxic function. To validate the feasibility of this pathway, we conducted repeated verification experiments including flow sorting, RNA fluorescence in situ hybridization (FISH), and immunohistochemistry. These experiments identified T cells expressing PSA/KLK3, further confirming that tumor cells educate T cells via exosomes to express tumor marker genes associated with tumor metastasis.

Does EV transfer affect nearby lymph nodes (LNs), and to what extent? We designed another single-cell sequencing experiment using prostate cancer lymph node metastasis samples (Batch2). Similarly, we found that T cells in all tumor samples generally highly expressed KLK3.

Interestingly, in one prostate cancer patient, both MRI imaging and postoperative pathology indicated negative results for the left and right external iliac lymph nodes. However, scRNA-Seq cell subpopulation statistics revealed the presence of T cells highly expressing KLK3 in the right lymph node tissue, suggesting the possible formation of a "pre-metastatic niche" structure. The left lymph node tissue not only contained T cells highly expressing KLK3 but also malignant epithelial cells, indicating the presence of a clinically unrecognized micro-metastasis in the left lymph node.

Besides this finding, we also noted that when Batch2 samples were processed using the BD Rhapsody platform for single-cell sorting, our analysis identified a population of neutrophils. In contrast, analysis of Batch1 data from the 10X Genomics sorting platform did not detect the presence of neutrophils.

We comprehensively described the high heterogeneity of stromal cells from multiple perspectives, including signature genes, pathways, and transcription factors.Within endothelial cells (ECs), we identified a subset highly expressing characteristic genes associated with cancer-associated fibroblasts (CAFs), naming them activated endothelial cells (aECs).
Cell communication analysis revealed strong interaction relationships between aECs and epithelial cells, suggesting their potential influence on tumor progression.

Therefore, we redesigned the experiment using 11 prostate cancer samples from different stages (Batch3), sorted endothelial cells, and performed single-cell sequencing. Pseudotime analysis showed that most aEC cells originated from CRPC samples, and their proportion gradually increased as the tumor progressed to CRPC, indicating a close association between the aEC subpopulation and the malignant progression of prostate cancer to CRPC.

Using flow sorting, we demonstrated that most aEC subpopulations originated from CRPC samples. Through co-culture experiments, we found that the aEC subpopulation could enhance the invasive ability of prostate cancer cells. These experiments collectively confirm that the aEC cell subpopulation plays an important role in the development and progression of prostate cancer.

This study, through ingenious experimental sample design, in-depth scRNA-Seq data analysis, and validation experiments, elucidated the potential molecular mechanisms of lymph node metastasis in prostate cancer. It discovered a new cell subtype associated with tumor progression, holding significant implications for novel therapeutic strategies and prognosis guidance in prostate cancer.

Link to Original Article:https://doi.org/10.1038/s41556-020-00613-6