Name: label-free identification of lymphocyte subtypes using three-dimensional quantitative phase imaging and machine learning
Uploaded: 2018-11-19T13:00:00
Description: Watch this Scientific Journal Video about Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning at JoVE.com

This work was supported by the KAIST BK21+ Program, Tomocube, Inc., and the National Research Foundation of Korea (2015R1A3A2066550, 2017M3C1A3013923, 2018K000396). Y. Jo acknowledges support from the KAIST Presidential Fellowship and Asan Foundation Biomedical Science Scholarship.

Animal care and experimental procedures were performed under the approval of the Institutional Animal Care and Use Committee of KAIST (KA2010-21, KA2014-01, and KA2015-03). All the experiments in this study were carried out in accordance with the approved guidelines.
1. Lymphocyte Isolation from Mouse Blood
<ol>
	<li>Once a C57BL/6J mouse is euthanized via CO2&#160;inhalation, insert a 26-G needle into the mouse heart and collect 0.3 mL of blood. Directly put blood into a tube wit...

We present a protocol that enables the label-free identification of lymphocyte types exploiting 3D quantitative phase imaging and machine learning. Critical steps of this protocol are quantitative phase imaging and feature selection. For the optimal holographic imaging, the density of cells should be controlled as described above. Mechanical stability of the cells is also important to obtain a precise 3D RI distribution because floating or vibrational cellular motions will disturb hologram measurements upon illumination ...

Lymphocytes can be classified into various subtypes including B, helper (CD4+) T, cytotoxic (CD8+) T, and regulatory T cells. Each lymphocyte type has a different role in the adaptive immune system; for example, B lymphocytes produce antibodies, whereas T lymphocytes detect specific antigens, eliminate abnormal cells, and regulate B lymphocytes. Lymphocyte function and regulation is tightly controlled by and related to various diseases including cancers1, autoimmune diseases2, and viral infections3. Thus, the identification of lymphocyte types is important to understand their pathophysiological roles in such diseases and for immunotherapy in clinics.
Currently, methods for classifying lymphocyte types rely on antigen-antibody reactions by targeting specific surface membrane proteins or surface markers4. Targeting surface markers is a precise and accurate method to determine lymphocyte types. However, it requires expensive reagents and time-consuming procedures. Furthermore, it carries risks of the modification of membrane protein structures and the alteration of cellular functions.
To overcome these challenges, the protocol described here introduces the label-free identification of lymphocyte types using 3D quantitative phase imaging (QPI) and machine learning5. This method enables the classification of lymphocyte types at a single-cell level based on morphological information extracted from label-free 3D imaging of individual lymphocytes. Unlike conventional fluorescence microscopy techniques, QPI utilizes refractive index (RI) distributions (intrinsic optical properties of live cells and tissues) as optical contrast6,7. The RI tomograms of individual lymphocytes represent phenotypic information specific to subtypes of lymphocytes. In this case, to systemically utilize 3D RI tomograms of individual lymphocytes, a supervised machine learning algorithm was utilized.
Using various QPI techniques, the 3D RI tomograms of cells have been actively used for the study of cell pathophysiology because they provide a label-free, quantitative imaging capability8,9,10,11,12,13. Also, the 3D RI distributions of individual cells can provide morphological, biochemical, and biomechanical information about cells. 3D RI tomograms have been previously utilized in the fields of hematology14,15,16,17, infectious diseases18,19,20, immunology21, cell biology22,23, inflammation24, cancer25, neuroscience26,27, developmental biology28, toxicology29, and microbiology12,30,31,32.
Although 3D RI tomograms provide detailed morphological and biochemical information of cells, the classification of lymphocyte subtypes is difficult to achieve by simply imaging 3D RI tomograms5. To systematically and quantitatively exploit the measured 3D RI tomograms for the cell type classification, we utilized a machine learning algorithm. Recently, several works have been reported in which quantitative phase images of cells were analyzed with various machine learning algorithms33, including the detection of microorganisms34, classification of bacterial genus35,36, rapid and label-free detection of anthrax spores37, automated analysis of sperm cells38, analysis of cancer cells39,40, and detection of macrophage activation41.
This protocol provides detailed steps to perform label-free identification of lymphocyte types at the individual cell level using 3D QPI and machine learning. This includes: 1) lymphocyte isolation from mouse blood, 2) lymphocyte sorting via flow cytometry, 3) 3D QPI, 4) quantitative feature extraction from 3D RI tomograms, and 5) supervised learning for identifying lymphocyte types.

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		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="33">
			<td height="33" style="height:33px;width:303px;">Mouse</td>
			<td style="width:175px;">Daehan Biolink</td>
			<td style="width:344px;">C57BL/6J mice&#160;</td>
			<td style="width:279px;">gender and age-matched, 6 &#8211; 8 weeks</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Falcon conical centrifuge tube</td>
			<td>ThermoFisher Scientific</td>
			<td>14-959-53A</td>
			<td>15 mL</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Phosphate-buffered saline&#160;</td>
			<td>Sigma-Aldrich</td>
			<td>806544-500ML</td>
			<td style="width:279px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Ammonium-chloride-potassium lysing buffer&#160;</td>
			<td>ThermoFisher Scientific</td>
			<td>A1049201</td>
			<td style="width:279px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">RPMI-1640 medium</td>
			<td>Sigma-Aldrich</td>
			<td>R8758</td>
			<td style="width:279px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Fetal bovine serum</td>
			<td>ThermoFisher Scientific</td>
			<td>10438018</td>
			<td style="width:279px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:303px;">Antibody</td>
			<td style="width:175px;">BD Biosciences</td>
			<td>553140 (RRID:AB_394655)</td>
			<td style="width:279px;">CD16/32 (clone 2.4G2)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>BD Biosciences</td>
			<td>555275 (RRID:AB_395699)</td>
			<td>CD3&#949; (clone 17A2)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>Biolegnd</td>
			<td>100734 (RRID:AB_2075238)</td>
			<td>CD8&#945; (clone 53-6.7)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>BD Biosciences</td>
			<td>557655 (RRID:AB_396770)</td>
			<td>CD19 (clone 1D3)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>BD Biosciences</td>
			<td>557683 (RRID:AB_396793)</td>
			<td>CD45R/B220 (clone RA3-6B2)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>BD Biosciences</td>
			<td>552878 (RRID:AB_394507)</td>
			<td>NK1.1 (clone PK136)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Antibody</td>
			<td>eBioscience</td>
			<td>11-0041-85 (RRID:AB_464893)</td>
			<td>CD4 (clone GK1.5)</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">DAPI&#160;</td>
			<td>Roche</td>
			<td>10236276001</td>
			<td style="width:279px;">4,6-diamidino-2-phenylindole</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Flow cytometry&#160;</td>
			<td>BD Biosciences</td>
			<td>Aria II or III&#160;</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Imaging chamber</td>
			<td>Tomocube, Inc.</td>
			<td>TomoDish</td>
			<td style="width:279px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Holotomography</td>
			<td>Tomocube, Inc.</td>
			<td>HT-1H</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Holotomography imaging software</td>
			<td>Tomocube, Inc.</td>
			<td>TomoStudio</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Image professing software</td>
			<td>MathWorks</td>
			<td>Matlab R2017b</td>
			<td></td>
		</tr>
	</tbody>
</table>

label-free identification of lymphocyte subtypes using three-dimensional quantitative phase imaging and machine learning

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

Figure 1&#160;shows the schematic process of the entire protocol. Using the procedure presented here, we isolated B (n = 149), CD4+ T (n = 95), and CD8+ T (n = 112) lymphocytes. To obtain phase and amplitude information at various angles of illumination, multiple 2D holograms of each lymphocyte were measured by changing the angle of illumination (from -60&#176; to 60&#176;). Typically, 50 holograms can be used to reconstruct a 3D RI tomogram, but the numb...

Watch this Scientific Journal Video about Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning at JoVE.com

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification ...

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