Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

Objective and Impact Statement.We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index(RI)tomography.Our computational approach that fully utilizes tomographic information of bone marrow(BM)white blood cell(WBC)enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.Introduction.Conventional methods for examining blood cells,such as blood smear analysis by medical professionals and fluorescence-activated cell sorting,require significant time,costs,and domain knowledge that could affect test results.While label-free imaging techniques that use a specimen’s intrinsic contrast(e.g.,multiphoton and Raman microscopy)have been used to characterize blood cells,their imaging procedures and instrumentations are relatively time-consuming and complex.Methods.The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network.We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors(n=10):monocyte,myelocyte,B lymphocyte,and T lymphocyte.The quantitative parameters of WBC are directly obtained from the tomograms.Results.Our results show>99%accuracy for the binary classification of myeloids and lymphoids and>96%accuracy for the four-type classification of B and T lymphocytes,monocyte,and myelocytes.The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique.Conclusion.We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows,providing cost-effective and rapid diagnosis for hematologic malignancy.

Expanded natural killer cells augment the antimyeloma effect of daratumumab, bortezomib, and dexamethasone in a mouse model

The use of natural killer(NK)cells is a promising and safe immunotherapeutic approach in the field of cancer immunotherapy.However,combination treatments are required to enhance the effector functions and therapeutic efficacy of NK cells.In this study,we investigated the potential of daratumumab(Dara),bortezomib,and dexamethasone(Dvd)to augment the antitumor effects of NK cells in a multiple myeloma(MM)xenograft mouse model.NK cells were expanded and activated using the K562-OX40 ligand and membrane-bound IL-18 and IL-21 in the presence of IL-2 and IL-15 from peripheral blood mononuclear cells from MM patients.A human MM xenograft model was established using human RPMI8226-RFP-FLuc cells in NOD/SCID IL-2Rγnull(NSG)mice.Tumor-bearing mice were divided into six treatment groups:no treatment,expanded NK cells(eNKs),Dara,Dara+eNKs,Dvd,and Dvd+eNKs.Dvd treatment strongly enhanced the cytotoxicity of eNKs by upregulating expression of NK cell activation ligands,downregulating expression of NK cell inhibitory ligands,and promoting antibody-dependent cellular cytotoxicity.The combination of eNKs with Dvd significantly prolonged mouse survival and reduced the tumor burden and serum M-protein level.Furthermore,Dvd pretreatment significantly increased eNK persistence and homing to MM sites.Our findings suggest that Dvd treatment potentiates the antimyeloma effects of NK cells expanded and activated ex vivo by modulating immune responses in MM-bearing mice.