Conventional methods for histopathologic tissue diagnosis are labor- and time-intensive and can delay decision-making during diagnostic and therapeutic procedures. We report the development of an automated and biocompatible handheld mass spectrometry device for rapid and nondestructive diagnosis of human cancer tissues. The device, named MasSpec Pen, enables controlled and automated delivery of a discrete water droplet to a tissue surface for efficient extraction of biomolecules. We used the MasSpec Pen for ex vivo molecular analysis of 20 human cancer thin tissue sections and 253 human patient tissue samples including normal and cancerous tissues from breast, lung, thyroid, and ovary. The mass spectra obtained presented rich molecular profiles characterized by a variety of potential cancer biomarkers identified as metabolites, lipids, and proteins. Statistical classifiers built from the histologically validated molecular database allowed cancer prediction with high sensitivity (96.4%), specificity (96.2%), and overall accuracy (96.3%), as well as prediction of benign and malignant thyroid tumors and different histologic subtypes of lung cancer. Notably, our classifier allowed accurate diagnosis of cancer in marginal tumor regions presenting mixed histologic composition. Last, we demonstrate that the MasSpec Pen is suited for in vivo cancer diagnosis during surgery performed in tumor-bearing mouse models, without causing any observable tissue harm or stress to the animal. Our results provide evidence that the MasSpec Pen could potentially be used as a clinical and intraoperative technology for ex vivo and in vivo cancer diagnosis.

Mass spectrometry imaging (MSI) has emerged as an important tool in the last decade and it is beginning to show potential to provide new information in many fields owing to its unique ability to acquire molecularly specific images and to provide multiplexed information, without the need for labeling or staining. In MSI, the chemical identity of molecules present on a surface is investigated as a function of spatial distribution. In addition to now standard methods involving MSI in vacuum, recently developed ambient ionization techniques allow MSI to be performed under atmospheric pressure on untreated samples outside the mass spectrometer. Here we review recent developments and applications of MSI emphasizing the ambient ionization techniques of desorption electrospray ionization (DESI), laser ablation electrospray ionization (LAESI), probe electrospray ionization (PESI), desorption atmospheric pressure photoionization (DAPPI), femtosecond laser desorption ionization (fs-LDI), laser electrospray mass spectrometry (LEMS), infrared laser ablation metastable-induced chemical ionization (IR-LAMICI), liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS), nanospray desorption electrospray ionization (nano-DESI), and plasma sources such as the low temperature plasma (LTP) probe and laser ablation coupled to flowing atmospheric-pressure afterglow (LA-FAPA). Included are discussions of some of the features of ambient MSI including the ability to implement chemical reactions with the goal of providing high abundance ions characteristic of specific compounds of interest and the use of tandem mass spectrometry to either map the distribution of targeted molecules with high specificity or to provide additional MS information in the structural identification of compounds. We also describe the role of bioinformatics in acquiring and interpreting the chemical and spatial information obtained through MSI, especially in biological applications for tissue diagnostic purposes. Finally, we discuss the challenges in ambient MSI and include perspectives on the future of the field.

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Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here we illustrate how gliomas can be rapidly classified by desorption electrospray mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was performed on thirty-six human glioma samples, including oligodendroglioma, astrocytoma and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Grey and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade and concentration features generated with lipidomic data showed high recognition capability with >97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 81% of tested features. Together, our findings offer proof of concept that intra-operative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors.