The controlled rotation of individual cells plays a crucial role in enabling three-dimensional multi-angle observation of cellular structure, function, and dynamic processes. Reported cell rotation techniques often struggle to strike a balance between high precision and simple control, and they exhibit limited control flexibility, typically achieving only uniaxial cell rotation. In this study, we propose a cell rotation technique in three dimensions based on optofluidics, which utilizes optical tweezers to immobilize the cell and exploits the asymmetry of the surrounding flow to drive cell rotation. By adjusting the focal position of the optical tweezers, cells can be positioned within various flow profiles, enabling control of the rotation speed, rotation direction, and rotation axis of cells. This approach simplifies the manipulation procedure, achieving desirable control precision and greater rotation flexibility. Using our approach, multi-angle surface imaging projections of target cells can be rapidly obtained, followed by capturing the cell contour data from the images. By combining the cell contour data with corresponding angular position information, we have reconstructed the 3D surface of the target cell. We have employed this technique in experiments for the analysis of red blood cell morphology. Based on the constructed 3D surface images of diverse-shaped red blood cells, we quantified structural parameters including cell surface area, volume, sphericity, and surface roughness, which demonstrates the potential application of this cell rotation technique for cellular morphological analysis.

Cell manipulation technology is an experimental approach aimed at precisely controlling and intervening in cell behavior through physical, chemical, or biological methods, facilitating the study of cell structure, function, interactions, and physiological processes.^{1,2} These techniques have been extensively applied in the fields of biology, medicine, and engineering, providing crucial tools for comprehension of cellular biology, disease etiology, and tissue engineering. Cell manipulation techniques include various modes such as translation, stretching, and rotation.^{3–12} Among them, the controllable rotation of cells has emerged as a current research focus due to its capability to provide multi-angle projection imaging of target cells, thereby providing meaningful information for morphological analysis.

Several cell rotation techniques based on optical fields, magnetic fields, acoustic fields, electric fields, and flow fields have been reported.^{13–17} The first three types of manipulation techniques are primarily achieved through energy-focusing methods, such as optical tweezers, magnetic tweezers, and acoustic tweezers. Although optical tweezers and magnetic tweezers can provide remarkably high precision in cell manipulation^{12,14,18} (such as cell movement, stretching, and rotation) and measurements,^{19} the complex driving mechanisms, the potential for high-density photodamage, incompatibility with larger cell sizes, and the risk of altering cell physiological characteristics due to magnetic bead attachment^{13} introduce limitations to practical applications. In contrast, acoustical tweezers offer excellent biocompatibility. However, the stability of acoustic waves generated by the high-frequency vibrations of microstructures and microbubbles is susceptible to disruption when propagating through a fluid, resulting in low spatial resolution and inadequate control precision of acoustic tweezers.^{15} The rotation of cells can be induced through the electric torque generated by the phase difference between the dipole moment of the cell and the applied electric field. By tuning the frequency of the AC signal, precise control of cell rotation can be achieved.^{16,20} This method offers high control precision and minimal cellular damage, but the fabrication of microchannel devices integrated with multiple electrodes, along with the associated high costs, presents a significant challenge. Constructing an asymmetric flow field around the target cell enables cell rotation by utilizing hydrodynamics.^{17} While this method provides significant driving forces, facilitating the rapid rotation of larger cells, challenges arise due to the instability of cell positioning during rotation and the complexity of flow field control, potentially limiting the capacity for multi-angle observation and measurement of the rotating cell. The aforementioned methods primarily achieve stable rotation of cells around a single axis or non-stable rotation around multiple axes (common in cell rotation driven by acoustic field and flow field). To achieve multi-axis controllable rotation of cells, the complex system (the number and location of field actuators) and sophisticated control procedures are challenges.

In this paper, we introduce a cell multi-axis controllable rotation technique based on optofluidics. This technology employs optical tweezers to trap cells and utilizes an asymmetric flow field to induce cell rotation around its center, ensuring positional stability during rotation. By adjusting the focus position of optical tweezers, the cell is positioned within varying flow profiles, changing the torque exerted on the cell and thereby enabling effective control over rotation speed, rotation direction, and rotation axis. In contrast to conventional optical tweezers and hydrodynamic-based methods, our approach provides synergistic advantages, including desirable control precision, simplified manipulation procedures, feasibility for multi-axis rotation, and compatibility with larger-sized cells. Utilizing the technique, we achieved controllable rotation of cells and obtained multi-angle cell imaging projections. Subsequently, employing the captured cell contours and recorded angular position data, we performed 3D surface imaging of target cells and measured their structural parameters such as surface area, volume, sphericity, and surface roughness.

*F*) and scattering force (

_{g}*F*) act on the centroid of the cell, with the former directed toward the focus of the optical tweezers and the latter directed along the direction of light propagation (

_{s}*z*-directional). The

*F*is counteracted by the

_{s}*z*-component of

*F*(

_{g}*F*). The fluid flow direction is denoted as

_{gz}*x*, and the cells within the microchannel experience a drag force (

*F*) exerted by the fluid, which consists of the pressure differential force (

_{d}*F*) and the shear force (

_{P}*F*). The

_{η}*F*is caused by the static pressure of the fluid around the cell. In rectilinear flow,

_{P}*F*passes through the centroid of the cell toward the

_{P}*x*-direction. The

*F*is generated due to the viscous traction exerted by the fluid and is proportional to the velocity gradient around the cell. Due to

_{η}*u*=

_{y}*u*= 0, and

_{z}*u*does not vary downstream, the

_{x}*τ*,

_{xx}*τ*,

_{yy}*τ*,

_{zz}*τ*, and

_{yz}*τ*in the shear stress tensor (

_{zy}*τ*, a 3 × 3 matrix) are equal to zero. Multiplying the shear stress tensor by the direction vector, the

*F*vector expression can be obtained as

_{η}*η*is the fluid viscosity, and

*dS*is the area of a cell surface element. Due to the cell's motion being confined to the

*y*and

*z*directions, the direction vector $ n \u2192$ is (0, 1, 1). The profile of the flow velocity

*u*for a rectangular cross section under laminar flow conditions is as follows:

_{x}*dp/dx*is the pressure gradient along the

*x*-direction, and 2

*w*and 2

*h*are the width and height of the channel cross section, respectively, (−

*w*≤

*y*≤ +

*w*, −

*h*≤

*z*≤

*h*).

When the cell is positioned at the geometric center of the channel cross section [(*y*, *z*) = (0, 0)], the flow velocity *u _{x}* around the cell is symmetric along the y axis and z axis, and the

*F*is also symmetric along the y axis and z axis (

_{η}*F*

_{ηy}_{1}=

*F*

_{ηy}_{2},

*F*

_{ηz}_{1}=

*F*

_{ηz}_{2}). In this case, the

*F*acting on the cell is directed toward the

_{d}*x*-direction through the cell's centroid. When the

*x*-component of

*F*(

_{g}*F*) =

_{gx}*F*, the resultant force on the cell becomes zero, and the cell is immobilized at a fixed position with a stationary state [Fig. 1(a)]. When the cell is offset along the +

_{d}*z*direction [Fig. 1(b)], the symmetry of

*u*and

_{x}*F*around the cell in the

_{η}*z*-direction is disrupted, where

*F*

_{ηz}_{1}<

*F*

_{ηz}_{2}. On the contrary, when the cell is offset toward the −

*z*direction,

*F*

_{η}_{1}

_{z}>

*F*

_{η}_{2}

_{z}. The asymmetric

*F*acting on the cell surface could generate a torque (±

_{ηz}*M*) and induce cell rotation motion around the y axis. Similarly, when the cell is displaced along the ±

_{y}*y*direction [Fig. 1(c)], the asymmetric

*F*generates ±

_{ηy}*z*-directional torque (±

*M*) on the cell, resulting in its rotation around the z axis. When the cell is simultaneously displaced along the y axis and the z axis, it experiences asymmetric forces in both directions [the case of

_{z}*F*

_{ηy}_{1}<

*F*

_{ηy}_{2},

*F*

_{ηz}_{1}<

*F*

_{ηz}_{2}is presented in Fig. 1(d)], consequently leading to a variation in the cell's rotational axis. Therefore, the regulation of the cell axis can be achieved by changing the position of cells.

The rotation rate of the cell is determined by the magnitude of the torque, which in turn is influenced by the asymmetry in the flow profile around the cell. The flow fields around cells positioned at different locations within the microchannel were simulated and analyzed using COMSOL, where the width of the microchannel cross section was set to 100 *μ*m, the height was set to 30 *μ*m, and the cell diameter was set to 10 *μ*m. ∇*u _{x}*|

_{a}and ∇

*u*|

_{x}_{b}represent the gradients of flow velocity

*u*at points

_{x}*a*and

*b*on opposite sides of the cell surface in the

*y–z*plane, with the line connecting the two points

*a*and

*b*forming an angle

*θ*with the y axis. In Fig. 1(e), the variation of ∇

_{a}

_{−}

_{b}(∇

_{a}

_{−}

_{b}= ∇

*u*|

_{x}_{a}− ∇

*u*|

_{x}_{b}) as

*θ*changes from 0 to 2

*π*is recorded. When ∇

_{a}

_{−}

_{b}= 0, the resultant torque produced by

*F*and

_{ηa}*F*is zero, at which point the cell rotates around the

_{ηb}*a*−

*b*line. It can be observed that when the cell is offset along the z axis, ∇

_{a}

_{−}

_{b}is equal to 0 at

*θ*=

*m**

*π*(m is an integer), and the cell rotates around the y axis. When the cell is offset along the y axis, ∇

_{a}

_{−}

_{b}is equal to 0 at

*θ*=

*n**

*π*/2 (n is an odd number), and the cell's axis of rotation is the z axis. When the cell is simultaneously offset in the

*y*and

*z*directions, the rotation axis of the cell is located in the

*y*–

*z*plane and forms an inclination angle of

*θ*|

_{∇}

_{a}

_{−}

_{b}= 0 with the y axis.

Figure 2(a) depicts the schematic diagram of the optofluidic-based cell rotation control platform. The optical system of the platform consisted of an inverted optical microscope (IX73, Olympus) and a 1064 nm wavelength single-mode fiber laser (AFL1064-37, Amonics), along with the corresponding optical components. The laser beam was expanded by a factor of 1.7 and then directed through a dichroic mirror into the microscope, where it was focused into the microfluidic channel using a water immersion objective (60×, NA = 1.2, Olympus) to create single-beam optical tweezers. A laser output power of 300 mW was utilized in the experiments, with a measured power of 180 mW at the entrance pupil. The microfluidic chip with microchannels was positioned on a piezo-stage (P-563PIMars, Physik Instrumente), allowing precise control of the relative position between the microchannel and the focus of optical tweezers. The inlet of the microchannel was connected to a pressure pump (Flow-EZ, Fluigent) to establish a stable flow field. The process of cell rotation was monitored using a camera (MV-SUA230GC-T, MindVision). Considering factors such as the size of cells (∼5–10 *μ*m), the risk of cell wall contact, and the complexity of fabrication, the microchannel was designed with a width of 100 *μ*m and a height of 30 *μ*m. The microfluidic chip was prepared using soft lithography techniques, bonding polydimethylsiloxane (PDMS) to a glass slide. To prevent cell adhesion and glass effect, the microchannels were coated with a solution of bovine serum albumin (3% w/v BSA in 1×buffer solution) and incubated for 30 min before conducting experiments.

In the experiment of cell rotation, initially, cells flowing in the suspension were trapped using optical tweezers, followed by flushing with phosphate-buffered saline (PBS, Spark Jade) to prevent interference from other cells during the trapped cell rotation. The microchannel was moved to the initial position [(*y*, *z*) = (0, 0)], where the trapped cell was located at the center of the microchannel cross section. The center of the y axis was adjusted based on the microscopic image, while the center of the z axis was determined based on the occurrence of cell rotation. By adjusting the piezoelectric stage, cell displacement was induced, and multi-axis controllable rotation of the cell was achieved. Figure 2(b) (Multimedia view) shows the rotation process of cells (the dynamic process can be seen in the multimedia view) around the y axis, the z axis, and an inclined axis situated within the *y–z* plane. Changing the cell coordinates results in variations in the cell rotation speed and the rotation axis. Due to limitations imposed by experimental conditions, measuring the inclination angle of the rotation axis was not feasible. Therefore, a conceptual demonstration is provided through the multimedia view. Figure 2(c) depicts the changes in the rotation speed of the cells with *y* and *z* position offset. It can be observed that when *z* = 0, the cell does not rotate. When the cell moves along the +*z* or −*z* direction, its rotation velocity increases in the opposite direction. At inlet pressure values of 7, 8, and 9 mbar, the maximum angular velocities obtained by the cell at *z* = +10/−10 *μ*m are 2.79, 4.76, and 12.08 rad/s, respectively. When the cell is positioned symmetrically on both sides of *z* = 0, its angular velocity variation exhibits symmetry. A distinct behavior is observed during cell displacement along the y axis compared with the movement along the z axis. The cell exhibits a relatively prolonged stationary state during this process, with rotation occurring only when |*y*| > 40 is reached. Subsequently, with an increase in |*y*|, the maximum angular velocities gradually rise to 2.31, 3.38, and 7.02 rad/s. Analysis suggests that the occurrence of this phenomenon could be attributed to the greater width (*y*-direction) of the microchannel (100 *μ*m) compared to its height (*z*-direction) (30 *μ*m), resulting in a flow velocity gradient in the *y*-direction (*∂ux*/*∂y*) that is smaller than the gradient in the *z*-direction (*∂ux*/*∂z*), particularly around *y =* 0. Consequently, when the cell is positioned within the range of (*y* = −40, *y* = +40), the disparity in *u _{x}* and

*F*between the two sides of the cell is relatively small, leading to a weaker torque provided by the flow force, which is insufficient to drive cell rotation.

_{η}The imaging plane of the cell is parallel to the *x*–*y* plane, enabling a 360° observation of the cell during rotation around the y axis. This allows for comprehensive structural data collection and 3D surface imaging. By adjusting the microchannel inlet pressure to 9 mbar and positioning the cell at [(*y*, *z*) = (0, 5)], the cell achieved an angular velocity of 6.54 rad/s. Based on this angular velocity, the angle of rotation between consecutive frames could be calculated. The completeness of the acquired cell structure data depends on the frame rate. The camera used in the experiment has a frame rate of 200 fps, with an average rotation angle of 0.03 rad between consecutive frames, satisfying the requirements for 3D surface imaging. Edge and contour features of the captured cell images by the camera were extracted through a series of steps. Initially, adaptive histogram equalization was employed to enhance the details of cell edge contours. Subsequently, a 3 × 3 median filter and a 5 × 5 Gaussian filter were utilized for noise reduction. The Otsu thresholding method was applied for image binarization. Further operations including filling, dilation, and erosion were performed to obtain continuous and complete cell edge contours. The obtained cell contours were combined with angle information and transformed into 3D coordinates of images using MATLAB. All contour points were plotted to form point cloud data (contains ∼35 000 points), which was then subjected to smoothing using the alpha-shape algorithm^{21,22} to accomplish the 3D reconstruction of the cell surface. Figures 3(a)–3(c) illustrates the 3D surface imaging procedure of three shapes of red blood cells (discocyte, echinocyte, and spherocyte). The red blood cells we employed were obtained from blood stored for one week, and the three different cell shapes result from varying degrees of storage lesion.

Combined with the magnification of the imaging system, the analysis of cell volume images enables the measurement of various structural parameters of the cells. In this experiment, we have conducted measurements of diverse-shaped red blood cells (discocytes, echinocytes, and spherocytes), including surface area (*S*), volume (*V*), sphericity [ $ S p= 36 \pi 1 / 3 \xb7 V 2 / 3 / S$], and surface roughness ( $ R y= D max\u2212 D min$, *D* represents the distance from a cell surface sampling point to the reference surface of the cell), with the results summarized in Fig. 4. The cell surface is segmented using triangles with an area of 1 × 10^{−4} *μ*m^{2}, and the surface area of the cell is obtained by summing the areas of all triangles. The cell is segmented using cubes with a volume of 1 × 10^{−6} *μ*m^{3}, and the sum of the volumes of all cubes approximates the cell's volume. Comparative analysis of the relevant parameters reveals distinct variations in the physical structures of red blood cells with different shapes. Among the three cell shapes, the average surface area of discocytes is the largest, measuring 139.07 *μ*m^{2}, while spherocytes exhibit the smallest average surface area of 108.62 *μ*m^{2}. The average volumes of discocytes, echinocytes, and spherocytes are 82.28, 78.12, and 74.95 *μ*m^{3}, respectively. Spherocytes have the highest sphericity of 0.77, whereas echinocytes show the maximum surface roughness at 0.34 *μ*m. This experiment validates the effectiveness of our technique in providing a valuable analytical tool for the study of cellular morphology.

The proposed optofluidic-based cell manipulation technique enables multi-axis controllable rotation of cells. In comparison to conventional cell rotation methods, our approach utilizes optical gradient forces to anchor the cell's rotation center and exploits fluidic asymmetry for driving cell rotation. Our approach eliminates the need for complex components and control mechanisms, providing simplicity and high-precision manipulation procedures, being compatible with various cell sizes and enabling the regulation of the cell rotation axis. Utilizing the high-precision rotation of cells to capture multi-angle imaging projections, coupled with recorded angular position information, enables 3D surface imaging and morphology analysis of cells. This multi-axis cell controllable rotation technique, in combination with advanced and diverse optical imaging techniques and image reconstruction algorithms, holds the potential to provide a practical solution for rapid and high-precision cell surface/volumetric imaging.

This study was supported by the University Natural Science Research Project of Anhui Province (No. KJ2021A0252), the Anhui Medical University Scientific Research Foundation (No. 2020xkj017), the Anhui Provincial Key Research and Development Plan (No. 2022a05020028), the Natural Science Foundation of Anhui Province (No. 2208085MC54), the Research Fund of Anhui Institute of Translational Medicine (No. 2021zhyx-B16), and the Key Scientific Research Foundation of Education Department of Anhui Province (Nos. 2022AH050676 and 2023AH040083).

## AUTHOR DECLARATIONS

### Conflict of Interest

The authors have no conflicts to disclose.

### Author Contributions

Yuxin Mao and Songlin Li contributed equally to this work.

**Yuxin Mao:** Conceptualization (equal); Data curation (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal). **Zhensheng Zhong:** Funding acquisition (equal); Investigation (equal). **Jinhua Zhou:** Conceptualization (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Supervision (equal); Writing – review & editing (equal). **Songlin Li:** Data curation (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Writing – original draft (equal); Writing – review & editing (equal). **Zixin Wang:** Data curation (equal); Investigation (equal); Methodology (equal); Writing – original draft (equal); Writing – review & editing (equal). **Meng Shao:** Data curation (equal); Investigation (equal). **Peng Wang:** Investigation (equal). **Xinyuan Tan:** Investigation (equal). **Fengya Lu:** Investigation (equal). **Yi Wang:** Investigation (equal). **Xunbin Wei:** Investigation (equal).

## DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding authors upon reasonable request.