High-Throughput Label-Free Isolation of Heterogeneous Circulating Tumor Cells and CTC Clusters from Non-Small-Cell Lung Cancer Patients.

(1) Background: Circulating tumor cell (CTC) clusters are emerging as clinically significant harbingers of metastases in solid organ cancers. Prior to engaging these CTC clusters in animal models of metastases, it is imperative for technology to identify them with high sensitivity. These clusters often present heterogeneous surface markers and current methods for isolation of clusters may fall short. (2) Methods: We applied an inertial microfluidic Labyrinth device for high-throughput, biomarker-independent, size-based isolation of CTCs/CTC clusters from patients with metastatic non-small-cell lung cancer (NSCLC). (3) Results: Using Labyrinth, CTCs (PanCK+/DAPI+/CD45-) were isolated from patients (n = 25). Heterogeneous CTC populations, including CTCs expressing epithelial (EpCAM), mesenchymal (Vimentin) or both markers were detected. CTCs were isolated from 100% of patients (417 +/- 1023 CTCs/mL). EpCAM- CTCs were significantly greater than EpCAM+ CTCs. Cell clusters of \u3e/=2 CTCs were observed in 96% of patients-of which, 75% were EpCAM-. CTCs revealed identical genetic aberrations as the primary tumor for RET, ROS1, and ALK genes using fluorescence in situ hybridization (FISH) analysis. (4) Conclusions: The Labyrinth device recovered heterogeneous CTCs in 100% and CTC clusters in 96% of patients with metastatic NSCLC. The majority of recovered CTCs/clusters were EpCAM-, suggesting that these would have been missed using traditional antibody-based capture methods

Feasibility of a handheld near infrared device for the qualitative analysis of bloodstains

One of the most common tasks in criminal investigation is to determine from which tissue source a biological fluid stain originates. As a result, there are many tests that are frequently used to determine if a stain is blood, semen or saliva by exploiting the properties of certain molecules present within the fluids themselves. These include chemical reagents such as the Kastle-Meyer or Acid Phosphatase tests, as well as other techniques like the use of alternative light sources. However, most of the tests currently available have some major drawbacks. In this study, a handheld near-infrared spectrometer is investigated for the specific identification of deposited bloodstains. First, a calibration was carried out by scanning over 500 positive (blood present) and negative (blood absent) samples to train several predictive models based on machine learning principles. These models were then tested on over 100 new positive and negative samples to evaluate their performance. All models tested were able to correctly classify deposited stains as blood in at least 81% of tested samples, with some models allowing for even higher classification accuracy at over 94%. This suggests that handheld near infrared devices could offer great opportunity for the rapid, low cost and non-destructive screening of body fluids at scenes of crime