Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells.

Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.

Estimated medical costs of methicillin-resistant Staphylococcus aureus infection classified by polymerase chain reaction-based open reading frame typing in Japan.

This retrospective, observational cohort study investigated the economic impact of genotype by classifying methicillin-resistant Staphylococcus aureus (MRSA) by using the polymerase chain reaction-based open reading frame typing (POT) method. Using administrative claims and bacteriological data for April 2016 to March 2021 from the University of Yamanashi Hospital, we ascertained the POT1 numbers and classified MRSA as either "hospital-derived" or "community-derived". We defined MRSA-associated medical practices and estimated the associated medical costs. After applying inverse probability of treatment weighting (IPTW)-based adjustment for patient characteristics between the two groups, we estimated the differences in medical costs during the "total therapy period" (defined as the interval from specimen submission to Day 42 after the susceptibility report) and the "definitive therapy period" (defined as the interval from susceptibility reporting to Day 42). Among the 135 MRSA-infected patients, 54 and 81 were classified as having hospital-derived and community-derived MRSA infections, respectively. Significant differences in patient characteristics were observed with regard to age (p = 0.0478), sex (p = 0.0422), surgery (p = 0.0349), chemotherapy (p = 0.0457) and immunosuppressive drug use (p = 0.0222). The median duration of the definitive therapy was 29 and 27 days, and the mortality rate during this period was 11% and 5% for the hospital-derived and community-derived types, respectively. After IPTW-based adjustment, the medical costs for the total therapy period were 324,480 and 296,462 Japanese yen (JPY) per patient for the hospital-derived and community-derived types, respectively, whereas the medical costs for the definitive therapy period were 279,635 and 256,542 JPY per patient for the hospital-derived and community-derived types, respectively. No statistically significant difference was detected (p = 0.5813 and p = 0.6355, respectively). In this study, MRSA healthcare costs were compared according to the POT scores, and no statistically significant differences were observed between hospital-derived and community-derived MRSA infections.

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

Virtual-freezing fluorescence imaging flow cytometry.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.