Analysis of human chromosomes by imaging flow cytometry

Chromosomal defects in multiple myeloma

Multiple myeloma is a plasma cell neoplasm driven by primary (e.g. hyperdiploidy; IGH translocations) and secondary (e.g. 1q21 gains/amplifications; del(17p); MYC translocations) chromosomal events. These are important to detect as they influence prognosis, therapeutic response and disease survival. Currently, cytogenetic testing is most commonly performed by interphase fluorescence in situ hybridisation (FISH) on aspirated bone marrow samples. A number of variations to FISH methodology are available, including prior plasma cell enrichment and incorporation of immunophenotypic plasma cell identification. Other molecular methods are increasingly being utilised to provide a genome-wide view at high resolution (e.g. single nucleotide polymorphism (SNP) microarray analysis) and these can detect abnormalities in most cases. Despite their wide application at diagnostic assessment, both FISH and SNP-array have relatively low sensitivity, limiting their use for identification of prognostically significant low-level sub-clones or for disease monitoring. Next-generation sequencing is increasingly being used to detect mutations and new FISH techniques such as by flow cytometry are in development and may address some of the current test limitations. Here we review the primary and secondary cytogenetic aberrations in myeloma and discuss the range of techniques available for their assessment.

Imaging flow cytometry-based multiplex FISH for three IGH translocations in multiple myeloma

Three types of chromosomal translocations, t(4;14)(p16;q32), t(14;16)(q32;q23), and t(11;14)(q13;q32), are associated with prognosis and the decision making of therapeutic strategy for multiple myeloma (MM). In this study, we developed a new diagnostic modality of the multiplex FISH in immunophenotyped cells in suspension (Immunophenotyped-Suspension-Multiplex (ISM)-FISH). For the ISM-FISH, we first subject cells in suspension to the immunostaining by anti-CD138 antibody and, then, to the hybridization with four different FISH probes for genes of IGH, FGFR3, MAF, and CCND1 tagged by different fluorescence in suspension. Then, cells are analyzed by the imaging flow cytometry MI-1000 combined with the FISH spot counting tool. By this system of the ISM-FISH, we can simultaneously examine the three chromosomal translocations, i.e, t(4;14), t(14;16), and t(11;14), in CD138-positive tumor cells in more than 2.5 × 104 nucleated cells with the sensitivity at least up to 1%, possibly up to 0.1%. The experiments on bone marrow nucleated cells (BMNCs) from 70 patients with MM or monoclonal gammopathy of undetermined significance demonstrated the promising qualitative diagnostic ability in detecting t(11;14), t(4;14), and t(14;16) of our ISM-FISH, which was more sensitive compared with standard double-color (DC) FISH examining 200 interphase cells with its best sensitivity up to 1.0%. Moreover, the ISM-FISH showed a positive concordance of 96.6% and negative concordance of 98.8% with standard DC-FISH examining 1000 interphase cells. In conclusion, the ISM-FISH is a rapid and reliable diagnostic tool for the simultaneous examination of three critically important IGH translocations, which may promote risk-adapted individualized therapy in MM.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.