Di Carlo et al. comment that our original results were insufficient to prove that the ghost cytometry technique is performing a morphologic analysis of cells in flow. We emphasize that the technique is primarily intended to acquire and classify morphological information of cells in a computationally efficient manner without reconstructing images. We provide additional supporting information, including images reconstructed from the compressive waveforms and a discussion of current and future throughput potentials. Ghost cytometry (GC) performs a direct analysis of compressive imaging waveforms and thereby substantially relieves the computational bottleneck hindering the realization of high-throughput cytometry based on morphological information (1). The comments by Di Carlo et al. argue against a number of our conclusions (2), but given the restricted length allowed for this response, we will address what we consider the most important points.

Ota et al. (Reports, 15 June 2018, p. 1246) report using pseudo-random optical masks and a spatial-temporal transformation to perform blur-free, high–frame rate imaging of cells in flow with a high signal-to-noise ratio. They also claim sorting at rates of 3000 cells per second, based on imaging data. The experiments conducted and results reported in their study are insufficient to support these conclusions. Ota et al. (1) proposed an approach to perform image-based flow cytometry and cell sorting that has attracted substantial attention because high throughput ( 3000 cells/s) and a high signal-to-noise ratio (SNR) were claimed. For example, on the basis of these assertions, the introductory commentary (2) referred to the system as an "ultrahigh-speed fluorescence imaging–activated cell sorter."

Ghost imaging was first observed with entangled photon pairs and viewed as a quantum phenomenon [1]. It acquires object information through the correlation calculations of the lightintensity fluctuations of two beams: object and reference [2, 3]. The object beam passes through the object and is detected using a single-pixel detector, and the reference beam does not interact with the object and is recorded using a multi-pixel detector with spatial resolution. It was experimentally demonstrated that ghost imaging can be achieved using only a single detector [4]. Computational ghost imaging is an imaging technique with which an object is imaged from light collected using a single-pixel detector with no spatial resolution [5, 6]. By replacing reference-beam measurements, it only requires a single-pixel detector, which simplifies the experiments in comparison to traditional two-detector ghost imaging. Using the signals and illumination pattern enables us to computationally reconstruct cell images. Let T (x, y) be a transmission function of an object. An object is illuminated by a speckle field generated by passing a laser beam through an optical diffuser, which is a material that diffuses light to transmit light.

Their work is published in Science. Cell sorting is a technique used in laboratories to separate complex mixtures of cells into their component cell types. Because certain cell types are very similar in size and shape, existing cell sorting methods may struggle with distinguishing one group of cells from another. In the present study, scientists at the University of Tokyo have invented a new cell identification and sorting system called ghost cytometry. In ghost cytometry, cells flow one at a time though a narrow channel underneath a single-pixel detector camera that senses the fluorescent light waves emitted by each cell.

Ghost imaging is a technique used to produce an object's image without using a spatially resolving detector. Here we develop a technique we term "ghost cytometry," an image-free ultrafast fluorescence "imaging" cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry.