Ultrasound frequency sonication facilitates high-throughput and uniform dissociation of cellular aggregates and tissues.

A sample preparation step involving dissociation of tissues into their component cells is often required to conduct analysis of nucleic acids and other constituents from tissue samples. Frequently, the extracellular matrix and cell-cell adhesions are disrupted via treatment with a chemical dissociating reagent or various mechanical forces. In this work, a new, high-throughput, multiplexed method of dissociating tissues and cellular aggregates into single cells using ultrasound frequency bath sonication is explored and characterized. Different operating parameters are evaluated, and a treatment protocol with potential for uniform, high-throughput tissue dissociation is compared to the existing best chemical and orbital plate shaking protocol. Metrics such as percent dissociation, cellular recovery, average aggregate size, proportion of various aggregate sizes, membrane circularity, and cellular viability are subsequently assessed and found to be favorable. In optimized conditions, 53 ±â€¯8% of 1 mm biopsy cores are dissociated within 30 min using sonication alone, surpassing leading high-throughput orbital plate shaking techniques five-fold. Chemical digestion is also 2 times more effective when complexed with sonication rather than orbital plate shaking. RNA content, quality, and expression are found to be superior to the standard protocol in terms of transcriptional preservation.

A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images.

Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.

Electric-field facilitated rapid and efficient dissociation of tissues Into viable single cells.

Single-Cell Analysis is a growing field that endeavors to obtain genetic profiles of individual cells. Disruption of cell-cell junctions and digestion of extracellular matrix in tissues requires tissue-specific mechanical and chemical dissociation protocols. Here, a new approach for dissociating tissues into constituent cells is described. Placing a tissue biopsy core within a liquid-filled cavity and applying an electric field between two parallel plate electrodes facilitates rapid dissociation of complex tissues into single cells. Different solution compositions, electric field strengths, and oscillation frequencies are investigated experimentally and with COMSOL Multiphysics. The method is compared with standard chemical and mechanical approaches for tissue dissociation. Treatment of tissue samples at 100 V/cm 1 kHz facilitated dissociation of 95 ± 4% of biopsy tissue sections in as little as 5 min, threefold faster than conventional chemical-mechanical techniques. The approach affords good dissociation of tissues into single cells while preserving cell viability, morphology, and cell cycle progression, suggesting utility for sample preparation of tissue specimens for direct Single-Cell Analysis.

Optimization of a Clinically Relevant Chemical-Mechanical Tissue Dissociation Workflow for Single-Cell Analysis.

INTRODUCTION: While single-cell analysis technology has flourished, obtaining single cells from complex tissues continues to be a challenge. Current methods require multiple steps and several hours of processing. This study investigates chemical and mechanical methods for clinically relevant preparation of single-cell suspension from frozen biopsy cores of complex tissues. The developed protocol can be completed in 15 min. METHODS: Frozen bovine liver biopsy cores were normalized by weight, dimension, and calculated cellular composition. Various chemical reagents were tested for their capability to dissociate the tissue via confocal microscopy, hemocytometry and quantitative flow cytometry. Images were processed using ImageJ. Quantitative flow cytometry with gating analysis was also used for the analysis of dissociation. Physical modeling simulations were conducted in COMSOL Multiphysics. RESULTS: A rapid method for tissue dissociation was developed for single-cell analysis techniques. The results of this study show that a combination of 1% type-1 collagenase and pronase or hyaluronidase in 100 U/µL HBSS solution is the most effective at dissociating 2.5 mm thawed bovine liver biopsy cores in 15 min, with dissociation efficiency of 37-42% and viability >90% as verified using live MDA-MB-231 cancer cells. Cellular dissociation is significantly improved by adding a controlled mechanical force during the chemical process, to dissociate 93 ± 8% of the entire tissue into single cells. CONCLUSIONS: Understanding cellular dissociation in ex vivo tissues is essential to the development of clinically relevant dissociation workflows. Controlled mechanical force in combination with chemical treatment produces high quality tissue dissociation. This research is relevant to the understanding and assessment of tissue dissociation and the establishment of an automated preparatory workflow for single cell diagnostics. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s12195-021-00667-y) contains supplementary material, which is available to authorized users.