Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Abstract

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Keywords: artificial intelligence; cell dynamics; heterogeneity; label-free; microscopy; open-source software.

Fig. 1

Future vision for label-free microscopy of heterogeneous and dynamic biological systems. 3D image stacks could be non-invasively generated deep within in vitro and in vivo systems at high-speed and resolution. Several label-free sources of contrast could be extracted from Raman spectra, fluorescence lifetimes, and phase shifts to define cell phenotypes (e.g., immune cell activation, cancer cell growth, stem cell differentiation) and behaviors (e.g., cell migration). Extracellular features including collagen content and morphology can also be visualized using label-free methods, such as second harmonic generation microscopy. Multivariate models including data visualization [e.g., uniform manifold approximation and projection (UMAP)] and predictive artificial intelligence (AI) models can be built from label-free sources of contrast to determine real-time function or predict future behavior within intact samples. Rightmost images depict dynamic changes in cell function captured with a label-free microscopy time series, including fibroblast activation (green), immune cell migration (blue), and cancer cell proliferation (red) within a heterogeneous tissue environment. Overall, this framework will provide single-cell information on molecular, functional, and structural features that will enable critical insights into dynamic, heterogeneous, living samples over multiple timescales.

Fig. 2

Applications of label-free microscopy in (a) cancer research, (b) autoimmune disease research, and in (c) cell and tissue engineering. (a) Line-field confocal OCT image (top) and corresponding histopathological examination (bottom) of a superficial basal cell carcinoma. Blue star: stratum corneum; white star: epidermis; yellow star: clusters of tumor cells; green star: cleft between tumor cell islands and dermis. Adapted with permission from Ref. . OCT was acquired with a supercontinuum laser at 800 nm center wavelength and 250 nm full width half maximum. (b) Raman spectral properties and 301 morphological features extracted from QPI classified different macrophage populations and their activation states, which are implicated in autoimmune diseases, with up to 97% sensitivity. Adapted with permission from Ref. . 780 nm laser diode was used for QPI light source. Raman spectroscopy was performed with 532 nm laser excitation. (c) CP OCT and multiphoton tomography (MPT) based on SHG and autofluorescence FLIM of NAD(P)H showed the formation of collagen fibers and increased oxidative metabolism in dermal papilla and fibroblast cells over 14 days of engineered skin tissue (dermal equivalent) development. MPT image shows interaction of collagen (green) and individual cells (red). Adapted with permission from Ref. . CP OCT was performed with a 1300 nm center wavelength source. 740 nm excitation wavelength was used for both SHG and FLIM of NAD(P)H, with detection range of 373 to 387 nm and 410 to 650 nm, respectively.