Most cancer deaths arise from metastases as a result of circulating tumor cells (CTCs) spreading from the primary tumor to vital organs. Despite progress in cancer prognosis, the role of CTCs in early disease diagnosis is unclear because of the low sensitivity of CTC assays. We demonstrate the high sensitivity of the Cytophone technology using an in vivo photoacoustic flow cytometry platform with a high pulse rate laser and focused ultrasound transducers for label-free detection of melanin-bearing CTCs in patients with melanoma. The transcutaneous delivery of laser pulses via intact skin to a blood vessel results in the generation of acoustic waves from CTCs, which are amplified by vapor nanobubbles around intrinsic melanin nanoclusters. The time-resolved detection of acoustic waves using fast signal processing algorithms makes photoacoustic data tolerant to skin pigmentation and motion. No CTC-associated signals within established thresholds were identified in 19 healthy volunteers, but 27 of 28 patients with melanoma displayed signals consistent with single, clustered, and likely rolling CTCs. The detection limit ranged down to 1 CTC/liter of blood, which is ~1000 times better than in preexisting assays. The Cytophone could detect individual CTCs at a concentration of ≥1 CTC/ml in 20 s and could also identify clots and CTC-clot emboli. The in vivo results were verified with six ex vivo methods. These data suggest the potential of in vivo blood testing with the Cytophone for early melanoma screening, assessment of disease recurrence, and monitoring of the physical destruction of CTCs through real-time CTC counting.
In vivo, Cytophone has demonstrated the capability for the early diagnosis of cancer, infection, and cardiovascular disorders through photoacoustic detection of circulating disease markers directly in the bloodstream with an unprecedented 1,000-fold improvement in sensitivity. Nevertheless, a Cytophone with higher specificity and portability is urgently needed. Here, we introduce a novel Cytophone platform that integrates a miniature multispectral laser diode array, time-color coding, and high-speed time-resolved signal processing. Using two-color (808 nm/915 nm) laser diodes, we demonstrated spectral identification of white and red clots, melanoma cells, and hemozoin in malaria-infected erythrocytes against a blood background and artifacts. Data from a Plasmodium yoelii murine model and cultured human P. falciparum were verified in vitro with confocal photothermal and fluorescent microscopy. With these techniques, we detected infected cells within 4 h after invasion, which makes hemozoin promising as a spectrally selective marker at the earliest stages of malaria progression. Along with the findings from our previous application of Cytophone with conventional lasers for the diagnosis of melanoma, bacteremia, sickle anemia, thrombosis, stroke, and abnormal hemoglobin forms, this current finding suggests the potential for the development of a portable rainbow Cytophone with multispectral laser diodes for the identification of these and other diseases.
Many diseases like cystic fibrosis and sickle cell anemia disease (SCD), among others, arise from single point mutations in the respective proteins. How a single point mutation might lead to a global devastating consequence on a protein remains an intellectual mystery. SCD is a genetic blood-related disorder resulting from mutations in the beta chain of the human hemoglobin protein (simply, β-globin), subsequently affecting the entire human body. Higher mortality and morbidity rates have been reported for patients with SCD, especially in sub-Saharan Africa. Clinical management of SCD often requires specialized interdisciplinary clinicians. SCD presents a major global burden, hence an improved understanding of how single point mutations in β-globin results in different phenotypes of SCD might offer insight into protein engineering, with potential therapeutic intervention in view. By use of mathematical modeling, we built a hierarchical (nested) graph-theoretic model for the β-globin. Subsequently, we quantified the network of interacting amino acid residues, representing them as molecular system of three distinct stages (levels) of interactions. Using our nested graph model, we studied the effect of virtual single point mutations in β-globin that results in varying phenotypes of SCD, visualized by unsupervised machine learning algorithm, the dendrogram.
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