Live Cell Imaging

Figure 3: Tomocube HT-2 combined HT and fluorescence microscope.

Te time-lapse 3D images here have provided important insights on the mechanism of Toxoplasma gondii infecting an ARPE-19 cell. Te ARPE-19 cell is shown dying, following penetration. Addition of fluorescence. Te intriguing prospects for

QPI with fluorescence correlative microscopy in LCI studies are indicated in a recent review published by Kim et al. [2]. Te group examined published studies of cell pathophysiology where 3D QPI had been used in combination with various fluo- rescence microscopy techniques. Taken together, these studies confirm the demand for more detailed morphological imaging of biological samples. In reviewing 2D work, Kim described how the axi- ally averaged RI of cells was determined and used for the

calculation of protein concentrations [3]. In another paper, the mitosis of a kidney cell was visualized through the quantitative phase image, fluorescence image, and their combined overlaid image [4]. Te review also highlighted the usefulness of simultaneous 3D QPI and 3D fluorescence imaging for cel- lular dynamics imaging, especially targeting the organelles, as phototoxicity and photo- bleaching can be minimized through QPI. For instance, Habaza et al. used 3D QPI and 3D confocal fluorescence imaging together for the study of yeast cells [5]. Also, optical diffraction tomography (ODT) and 3D fluo- rescence correlative imaging have been used in RI and fluorescence tomography with optofluidic rotation (RAFTOR), which ana-

lyzed suspended cells quantitatively with molecular specific- ity [6]. HT combined with 3D fluorescence images also was used by Kim et al. [7] to image live cells for quantitative and precise spatial molecular specificity. Te power of QPI, and of combining this with fluorescence

in a single instrument, is shown in Figure 5. Tis figure shows NIH-3T3 cells in both a QPI image, a 3D fluorescence (GFP- Mito and mCherry-Golgi) image, and an overlay of these two images. QPI, deep learning, and the fight against cancer. Some

of the latest work to be published using HT exploits the com- puting power that accompanies many high-end microscopy systems. Investigating the 4D interaction dynamics of T cells and their targets in the immunological synapse (IS), the joint team from Tomocube and the Korea Advanced Institute of Science and Technology (KAIST) used the reconstructed RI map from the HT-2 microscope (see Figure 6) as input for a machine-learning model to segment CD19-positive K562 cells and CD19-specific chimeric antigen receptor T cells (CAR-T19 cells), considered in some circles as the next-gen- eration, personalized, anti-cancer treatment [8]. Teir results can be found at bioRxiv ( Te study acquired 3D tomograms of the cells every 500 milli- seconds to capture the RI and corresponding total protein density distributions (Figure 6). Ten, the real-time, 3D analysis of the IS between the interacting cells was executed by the deep learning network. Te result is automatic and quantitative spatiotemporal analyses of IS kinetics, together with morphological and biochemical parameters related to the total protein densities of immune cells. Tis is of special interest given that many research teams around the world are focused on the study of the IS as they try to understand at the nano-scale how immune cells recognize pathogens and malignant cancer cells. Although it is a highly complex and potentially risky

Figure 4: Time-lapse 3D images of Toxoplasma gondii infecting an ARPE-19 cell, which undergoes death as it is penetrated. Time-lapse 3D images can provide insights into the mechanism of infection. Tomographs acquired with the Tomocube HT-1 HT microscope.

2020 January •

treatment, CAR-T is the first therapy specifically developed for each individual patient and involves reprogramming the patient’s own immune system. Te CAR-T treatment takes T-cells from patients and modifies them using advanced genetic engineering to create chimeric antigen receptors (CARs), which program the T-cells to find and destroy cancer cells when re-introduced.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56