Mass spectrometry imaging (MSI) enables label-free mapping
of hundreds
of molecules in biological samples with high sensitivity and unprecedented
specificity. Conventional MSI experiments are relatively slow, limiting
their utility for applications requiring rapid data acquisition, such
as intraoperative tissue analysis or 3D imaging. Recent advances in
MSI technology focus on improving the spatial resolution and molecular
coverage, further increasing the acquisition time. Herein, a deep
learning approach for dynamic sampling (DLADS) was employed to reduce
the number of required measurements, thereby improving the throughput
of MSI experiments in comparison with conventional methods. DLADS
trains a deep learning model to dynamically predict molecularly informative
tissue locations for active mass spectra sampling and reconstructs
high-fidelity molecular images using only the sparsely sampled information.
Experimental hardware and software integration of DLADS with nanospray
desorption electrospray ionization (nano-DESI) MSI is reported for
the first time, which demonstrates a 2.3-fold improvement in throughput
for a linewise acquisition mode. Meanwhile, simulations indicate that
a 5–10-fold throughput improvement may be achieved using the
pointwise acquisition mode.