Background/Aims. Microelectrode arrays (MEAs) realized on and operated by CMOS integrated circuits, typically containing thousands of densely packed electrodes, can record neural activities with high spatial (e.g., ~15 µm) and high temporal resolution (e.g., ~20 kHz bandwidth) [1-3]. The recorded voltage signals are accompanied by noise of biological and electronic origins, and by filtering out this noise, one can see electrophysiological signals, in particular, action potentials, more clearly. However, the noise may be actually exploited for the label-free and noninvasive detection of adherent cells, based on the fact that the voltage noise from the resistive adhesion cleft may vary depending on whether the cells adhere or not [4]. The cell adhesion noise (CAN) might be altered by the electrode size [5], the size of the adherent cells, the junction capacitance, the cell type, and the corresponding sampling frequency [6]. We therefore record the voltage noise of adherent cell cultures from different types of CMOS MEAs and their corresponding recording systems, each of them being optimized in a different working regime. Based on previous studies with neurons, the cleft resistance between a cell and a micron-scale recording site contributes significantly to the voltage noise, thus distinguishing this value from the one of a bare sensor site [4, 7-8]. We analyze the voltage noise in terms of power spectral density (SV) to generate electrical images from the SV-derived CAN maps with single-cell resolution to assess the reliability of the label-free cell imaging across different CMOS MEA biosensing platforms. This label-free cell detection is of broad interest for biotechnological applications, for instance, to determine the cells’ proliferation status [9] or to record sparsely distributed regions on the MEA, where densely packed neural cells require high-density arrangements [10]. In contrast to optical imaging or standard biological dead-end assays, CAN spectroscopy offers label-free and, in principle, continuous recording capability and could be used to track the neural system as it adheres to the MEA surface. Methods. The colorectal cancer (CRC) cells and E18 primary neuron cells were plated in monocultures on the CMOS MEAs coated with collagen type I and poly-D-lysine. We analyzed the recorded voltage noise power spectral density SV at 35 kHz. After detecting adherent cells, the CAN-based electrical images were compared with light microscopic images to relate the estimated cell positions to ground truth [9]. Results. We calculated the root-mean-square (RMS) and the SV of the noise levels of CMOS MEAs on the recording platforms supplied with PBS (1X) of conductivity κ=16 mS/cm. The average RMS voltage noise of hundreds of sensor sites of CMOS MEA type I showed 15 µV (in accordance with [11]) with an SV of 0.0009 µV²/Hz. The CMOS MEA type II was at 57 µVRMS (in accordance with [12]) with SV of 0.05 µV²/Hz and CMOS MEA type III at 75.8 µVRMS with SV of 0.013 µV²/Hz (s. Figure 1). The adhesion noise spectrum from different sensor sites with adherent cells consistently shows uniform profiles with elevated values compared to sensors without cells. We reproducibly accomplished adhesion noise-based cell identification across different CMOS MEA biosensing platforms independent of the noise levels thereof with high correspondence (>80 %) between electrically and microscopically estimated cell positions. Conclusion. Adhesion noise spectroscopy constitutes a potent tool for label-free and noninvasive cell detection with high accordance between electrical and brightfield microscopy imaging. Future work aims to record neural activity at a resolution of 6 µm and to detect cancer spheroids and organoids.