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. 2021 May 25;12(1):3079.
doi: 10.1038/s41467-021-23416-1.

Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry

Soo-Yeon Cho  1 , Xun Gong  1 , Volodymyr B Koman  1 , Matthias Kuehne  1 , Sun Jin Moon  1 , Manki Son  1 , Tedrick Thomas Salim Lew  1   2 , Pavlo Gordiichuk  1 , Xiaojia Jin  1 , Hadley D Sikes  1 , Michael S Strano  3
Affiliations

Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry

Soo-Yeon Cho et al. Nat Commun. .

Abstract

Nanosensors have proven to be powerful tools to monitor single cells, achieving spatiotemporal precision even at molecular level. However, there has not been way of extending this approach to statistically relevant numbers of living cells. Herein, we design and fabricate nanosensor array in microfluidics that addresses this limitation, creating a Nanosensor Chemical Cytometry (NCC). nIR fluorescent carbon nanotube array is integrated along microfluidic channel through which flowing cells is guided. We can utilize the flowing cell itself as highly informative Gaussian lenses projecting nIR profiles and extract rich information. This unique biophotonic waveguide allows for quantified cross-correlation of biomolecular information with various physical properties and creates label-free chemical cytometer for cellular heterogeneity measurement. As an example, the NCC can profile the immune heterogeneities of human monocyte populations at attomolar sensitivity in completely non-destructive and real-time manner with rate of ~600 cells/hr, highest range demonstrated to date for state-of-the-art chemical cytometry.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Nanosensor integration with microfluidics.
a Schematic illustration of the nanosensor integration process with microfluidics using EISA. b Photograph of EISA process of NIM for 0 min (left) and 30 min (right). c Photograph of completed multi-array NIM and pristine channel. d Polarized Raman spectrum (G-peak) of NIM. e nIR images of NIM and pristine channel. f Magnified nIR image of NIM with single-cell size resolution (20 µm) having ~720 nIR reporter pixel. g Histograms of nIR pixel intensities of top and bottom NIM surfaces NIM (inset: nIR images of the top and bottom surfaces). h nIR fluorescence spectrum of NIM. i nIR images of NIM with varying composition of SWNT nanosensor integration.
Fig. 2
Fig. 2. In vitro chemical detection performances of NIM.
a nIR spectrum of NIM with H2O2 solution flowing (1 µM, 1 µL/min). b nIR images of NIM before and after H2O2 flowing (1 M, 10 µL/min, 10 min). c Schematic illustration of H2O2 detection mechanism of SWNT/(GT)15 nanosensor. d Real-time nIR response of NIM with various concentrations (10−6, 10−5, 10−4, 10−3, 10−2, 10−1, 100 M) of H2O2 injection (10 min). e Maximum response amplitude and f response time of NIM with various concentrations of H2O2. The data represent the mean value of 250 × 350 µm2 NIM measurement. g nIR snapshots and intensity histogram (fire scale, ImageJ) of NIM with single-cell size resolution (20 µm) after 10 min flowing of various concentrations of H2O2.
Fig. 3
Fig. 3. Cellular lensing effect.
a Instrumental setup for NCC implementation: schematic illustration (left) and a photograph (right). b nIR images of human monocytes flowing (0.5 µL/min) NIM. c Magnified nIR image of single monocyte in NIM (inset: OM image of single monocyte). d FDTD numerical modeling for photonic nanojet and fitting with experimental cellular lensing profile (nc/nm = 1.04, λ = 1 µm). e nIR lensing profiles of a single cell with various focusing points from 5 to 100 µm along Z-stage. nIR lensing effects of f various live cells and g reference micro-particles (top-to-bottom: schematics, OM, nIR images, lensing profiles). h FWHM and i enhancement factors of various cells with numerical model. Data are mean (circle) ± σ (error bar), with ncell = 10. j Schematic illustrations for different lensing behavior of a high RI cell (left) and a low RI cell (right). Scale bars: 20 µm.
Fig. 4
Fig. 4. Real-time chemical efflux monitoring using the cellular lensing effect.
a Time-series nIR images of a stationary single monocyte with different immune activation states (−PMA, +PMA, +PMA & catalase). b Real-time nIR intensity variations of the cells with different activation states. c Schematic illustrations of H2O2 efflux monitoring mechanism with nIR lensing effect. d 3D diffusion and reaction kinetic modeling for translation of measured nIR signals to real-time local H2O2 concentration. e Real-time H2O2 efflux profiles of each single monocyte estimated by the model. 16-color scalebars represent nIR intensity from white (16833) to dark blue (0). Scale bars: 20 µm.
Fig. 5
Fig. 5. NCC for monitoring of multimodal immune response heterogeneities.
a Schematics and nIR images of NCC set up with distinct activation of human monocytes (−PMA and +PMA). b Automatic nIR image analysis using computational code for cell data extractions. c–e NCC cytometry plots of H2O2 efflux rate vs. biophysical parameters ((c) size (2D projected area), (d) eccentricity, (e) RI) of two monocytes populations. Data are ncell = 413 for −PMA, ncell = 414 for +PMA from n = 6 biologically independent samples. f NCC distribution curves of H2O2 efflux rates with data from commercial assay kit. g–i NCC cytometry plots for cell biophysical parameters ((g) eccentricity vs. size, (h) RI vs. eccentricity, (i) size vs RI). j–l NCC distribution curves of each biophysical parameters ((j) size, (k) eccentricity, (l) RI). m Schematics illustrations for cell properties variations of human monocyte populations with immune activations. Scale bars: 20 µm.
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