Assessing cell viability with dynamic optical coherence microscopy

Abstract

Assessing cell viability is important in many fields of research. Current optical methods to assess cell viability typically involve fluorescent dyes, which are often less reliable and have poor permeability in primary tissues. Dynamic optical coherence microscopy (dOCM) is an emerging tool that provides label-free contrast reflecting changes in cellular metabolism. In this work, we compare the live contrast obtained from dOCM to viability dyes, and for the first time to our knowledge, demonstrate that dOCM can distinguish live cells from dead cells in murine syngeneic tumors. We further demonstrate a strong correlation between dOCM live contrast and optical redox ratio by metabolic imaging in primary mouse liver tissue. The dOCM technique opens a new avenue to apply label-free imaging to assess the effects of immuno-oncology agents, targeted therapies, chemotherapy, and cell therapies using live tumor tissues.

Fig. 1.

Cell viability imaging protocol using dOCM. (a) OCM and dOCM images of the same cross-section from a CT26 tumor fragment. Green and magenta circles represent live and dead regions of interest as indicated in (b). (b) Upper panel: Intensity profiles of live (green) and dead (magenta) regions from the CT26 tumor fragment over 6.75 s. Lower panel: power spectral functions of the live (green) and dead (magenta) regions. The live frequency in this paper is defined as 0.1 Hz – 1 Hz. (c) Volumetric dOCM imaging protocol. 256 frames per cross section over 6.75 s are collected. (d) Single plane views of a CT26 tumor fragment. Yellow lines indicate the positions of adjacent xz and yz images. The upper right corner of the fragment contains numerous live cells, whereas the rest of the tissue contains scattered live cells. Scale bars: 100 µm.

Fig. 2.

Multimodal imaging of cell viability from dOCM and propidium iodide in CT26 syngeneic tumor tissue. (a) Multimodal imaging protocol. Local death in the center is introduced by microinjection of 3% H₂O₂ with a micromanipulator in CT26 fragments. Volumetric data of the fragment is collected by dOCM first and then by MPM after being flipped. (b) Registered propidium iodide (PI, magenta) and dOCM (green) images of the same fragment (also see Supplement 1). The images are both averaged intensity projection of 100 µm over depth. Scale bar: 100 µm. (c) Receiver Operating Characteristic (ROC) curve of dOCM contrast for live/dead status when PI is used as ground truth. The area under ROC is 0.9099.

Fig. 3.

The correlaion of cell viability derived from dOCM and propidium iodide. Each dot represents one experiment. The live percentage per fragment is calculated by dOCM (x-axis) and propidium iodide (y-axis), separately. The slope from linear fitting is 1.1 with a R² value of 0.99 over 5 experiments. The blue dashed line is the diagonal. The black line is the flitted curve.

Fig. 4.

Multimodal imaging of cell viability from dOCM and optical redox ratio via multiphoton microscopy in primary mouse liver tissue. Three different death mechanisms (a) are studied by mechanical damage (necrosis), local injection of staurosporine (apoptosis), and shikonin (necroptosis). dOCM (b) and optical redox ratio (c) images of the same mouse liver tissue. The optical redox ratio is indicated by the color bar in (c). Scale bars: 100 µm.

Fig. 5.

The correlation of cell viability derived from dOCM and optical redox ratio. Each dot represents one experiment. The live percentage per fragment is calculated by dOCM (x-axis) and redox ratio (y-axis), separately. The slope from linear fitting is 0.87 with a R² value of 0.91 over 9 experiments including necrosis, apoptosis, and necroptosis (Fig. 4). The blue dashed line is the diagonal. The black line is the flitted curve.