Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

Nanoscale topological imaging using atomic force microscopy (AFM) combined with infrared (IR) spectroscopy (AFM-IR) is a rapidly emerging modality to record correlated structural and chemical images. Although the expectation is that the spectral data faithfully represents the underlying chemical composition, the sample mechanical properties affect the recorded data (known as the probe-sample-interaction effect). Although experts in the field are aware of this effect, the contribution is not fully understood. Further, when the sample properties are not well-known or when AFM-IR experiments are conducted by nonexperts, there is a chance that these nonmolecular properties may affect analytical measurements in an uncertain manner. Techniques such as resonance-enhanced imaging and normalization of the IR signal using ratios might improve fidelity of recorded data, but they are not universally effective. Here, we provide a fully analytical model that relates cantilever response to the local sample expansion which opens several avenues. We demonstrate a new method for removing probe-sample-interaction effects in AFM-IR images by measuring the cantilever responsivity using a mechanically induced, out-of-plane sample vibration. This method is then applied to model polymers and mammary epithelial cells to show improvements in sensitivity, accuracy, and repeatability for measuring soft matter when compared to the current state of the art (resonance-enhanced operation). Understanding of the sample-dependent cantilever responsivity is an essential addition to AFM-IR imaging if the identification of chemical features at nanoscale resolutions is to be realized for arbitrary samples.

Infrared-Optical Hybrid Microscopy (IR-OH) provides mid-IR imaging on the visible microscope platform based on photothermal expansion. Spectral IR imaging of full tissue slides is demonstrated for automated disease diagnosis and computational H&E staining.

No abstract