High throughput, label-free isolation of circulating tumor cell clusters in meshed microwells

Boya, Mert; Ozkaya‐Ahmadov, Tevhide; Swain, Brandi E.; Chu, Chia‐Heng; Asmare, Norh; Civelekoglu, Ozgun; Lee, Dohwan; Tobia, Sherry; Biliya, Shweta; McDonald, Laura; Nazha, Bassel; Küçük, Ömer; Sanda, Martin G.; Benigno, Benedict B.; Moreno, Carlos S.; Bilen, Mehmet Asım; McDonald, John F.; Sarioglu, A. Fatih

doi:10.1038/s41467-022-31009-9

Cited by 46 publications

(35 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Resultsmentioning

confidence: 99%

“…In addition to single CTCs, CTCs can be found in the blood as cell aggregates, known as CTC clusters composed of several CTCs or CTCs and neutrophils 25 . These CTC clusters have differential biological features such as an enhanced survival and metastatic potential 25 . To challenge CTC-Tracer on a complex task with various types of RNA-seq data of CTCs, we applied it to a recently derived complex dataset.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Deep transfer learning enables lesion tracing of circulating tumor cells

Guo

Song

et al. 2022

Nat Commun

View full text Add to dashboard Cite

Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Single-cell RNA sequencing (scRNA-seq) is a powerful technology for cell characterization. Integrating scRNA-seq into a CTC-focused liquid biopsy study can perhaps classify CTCs by their original lesions. However, the lack of CTC scRNA-seq data accumulation and prior knowledge hinders further development. Therefore, we design CTC-Tracer, a transfer learning-based algorithm, to correct the distributional shift between primary cancer cells and CTCs to transfer lesion labels from the primary cancer cell atlas to CTCs. The robustness and accuracy of CTC-Tracer are validated by 8 individual standard datasets. We apply CTC-Tracer on a complex dataset consisting of RNA-seq profiles of single CTCs, CTC clusters from a BRCA patient, and two xenografts, and demonstrate that CTC-Tracer has potential in knowledge transfer between different types of RNA-seq data of lesions and CTCs.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Deep transfer learning enables lesion tracing of circulating tumor cells

Guo

Song

et al. 2022

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Currently, the commonly used multi-cell population capture and release methods tend to lose effective information of heterogeneity, thus failing to realize the analysis and study of the CTC genome, proteome, and RNA at different stages of tumor metastasis and the evaluation of anti-tumor therapy and drug resistance at different stages. ,− To solve this problem, methods like high-purity capture of CTCs and non-destructive site release of CTCs are conducted to analyze heterogeneous single cells maintaining the activity and privity at a specific stage. , In this process, some advanced technologies are developed: (1) Microfluidic chips, to realize precise manipulation and analysis of single cells, which gradually becomes an important platform for cell biology research. ,− (2) Gradient magnetic field sorting can separate magnetic labeled cells into different subpopulations based on cell surface antigen expression levels. − (3) Light-responsive intelligent interfaces have been increasingly used to regulate cell adhesion behavior due to their advantages of non-invasiveness, little disturbance to biological systems, and high temporal and spatial resolution. − Combining the microfluidic chip, gradient magnetic field, and light-responsive intelligent interface to give full play to their respective advantages in the analysis of CTCs, the rapid, high-purity, and accurate analysis of CTCs in blood samples can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Light-Responsive Size-Selective Lateral Flow Chip for Single-Cell Manipulation of Circulating Tumor Cells

Zheng

Chen

et al. 2022

Anal. Chem.

View full text Add to dashboard Cite

Accurately obtaining information on the heterogeneity of CTCs at the single-cell level is a very challenging task that may facilitate cancer pathogenesis research and personalized therapy. However, commonly used multicellular population capture and release assays tend to lose effective information on heterogeneity and cannot accurately assess molecular-level studies and drug resistance assessment of CTCs in different stages of tumor metastasis. Herein, we designed a near-infrared (NIR) light-responsive microfluidic chip for biocompatible single-cell manipulation and study the heterogeneity of CTCs by a combination of the lateral flow microarray (LFM) chip and photothermal response system. First, immunomagnetic labeling and a gradient magnetic field were combined to distribute CTCs in different regions of the chip according to the content of surface markers. Subsequently, the LFM chip achieves high single-cell capture efficiency and purity (even as low as 5 CTCs per milliliter of blood) under the influence of lateral fluid and magnetic fields. Due to the rapid dissolution of the gelatin capture structure at 37 °C and the photothermal properties of gold nanorods, the captured single CTC cell can be recovered in large quantities at physiological temperature or released individually at a specific point by NIR. The multifunctional NIR-responsive LFM chip demonstrates excellent performance in capture and site release of CTCs with high viability, which provides a robust and versatile means for CTCs heterogeneity study at the single-cell level.

show abstract

“…In general, CTCs are extremely rare in blood samples and range from one to a hundred cells in a 7.5 mL tube of human blood, depending on the stage of the disease [9]. CTC clusters are even rarer and constitute only 2-5% of all CTCs [17]. However, CTC clusters are disproportionately e cient at seeding metastases.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, CTCs and CTC clusters are valuable biomarkers to determine prognosis, monitor therapy, assess risk of recurrence, and possibly in the future to aid in early cancer detection and screening [3][4][5][6][7][8][9][10][11][12]. Over the past decade, numerous CTC capturing techniques have been developed [17,[18][19][20]. The different enrichment and isolation processes either leverage the morphological characteristics of CTCs which include size/deformability-based separation [21][22][23] and density-gradient centrifugation [24,25], or rely on immunoa nity-based separation through targeting speci c cell surface epitope expression [26,27].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Circulating Tumor Cells and Cancer Associated Fibroblasts using Deep Learning

Cheng

Rawal

Brown

et al. 2022

Preprint

View full text Add to dashboard Cite

Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs from the uniformly in-focus images. Our deep learning method achieved precision and recall of 94% and 96% for mCTC detection, and 90% and 83% for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% and 79% for mCTC and 57% and 54% for CAF. Our custom imaging system combined with deep learning cell identification method represents a significant advance on CTC and CAF analysis.

show abstract

High throughput, label-free isolation of circulating tumor cell clusters in meshed microwells

Cited by 46 publications

References 57 publications

Deep transfer learning enables lesion tracing of circulating tumor cells

Deep transfer learning enables lesion tracing of circulating tumor cells

Near-Infrared Light-Responsive Size-Selective Lateral Flow Chip for Single-Cell Manipulation of Circulating Tumor Cells

Automatic Detection of Circulating Tumor Cells and Cancer Associated Fibroblasts using Deep Learning

Contact Info

Product

Resources

About