Christopher E. Arcadia

In this article, we introduce a flexible technique for high-throughput solid-state nanopore analysis of single biomolecules. By confining the electrolyte to a micron-scale liquid meniscus at the tip of a glass micropipette, we enable automation and reuse of a single solid-state membrane chip for measurements with hundreds of distinct nanopores per day. In addition to overcoming important experimental bottlenecks, the microscale liquid contact dramatically reduces device capacitance, which is a key limiting factor to the speed and fidelity of solid-state nanopore sensor recordings.

Multicomponent reactions enable the synthesis of large molecular libraries from relatively few inputs. This scalability has led to the broad adoption of these reactions by the pharmaceutical industry. Here, we employ the four-component Ugi reaction to demonstrate that multicomponent reactions can provide a basis for large-scale molecular data storage. Using this combinatorial chemistry we encode more than 1.8 million bits of art historical images, including a Cubist drawing by Picasso. Digital data is written using robotically synthesized libraries of Ugi products, and the files are read back using mass spectrometry. We combine sparse mixture mapping with supervised learning to achieve bit error rates as low as 0.11% for single reads, without library purification. In addition to improved scaling of non-biological molecular data storage, these demonstrations offer an information-centric perspective on the high-throughput synthesis and screening of small-molecule libraries.

This letter presents a large-scale fully integrated multimodal sensor array for biological imaging. The 512×256 sensor array can perform spatially resolved electrochemical impedance spectroscopy (EIS) with switching frequencies up to 100 MHz, acquire multicolor optical images, and sense pH using titanium nitride (TiN) ion sensitive field effect transistors (ISFETs). The chip features code-division multiplexed (CDM) readout of groups of pixels simultaneously, enabling extended integration times at a given frame rate. The system is implemented in 180-nm CMOS with 9.5 μm × 11.5 μm pixels. Its overall fill factor is 57%, including peripheral control and readout circuits, yielding a widefield spatially resolved multimodal biosensing platform for advanced cell culture applications.

Biomolecular information systems offer exciting potential advantages and opportunities to complement conventional semiconductor technologies. Much attention has been paid to information-encoding polymers, but small molecules also play important roles in biochemical information systems. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of small-molecule postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic mixtures of metabolites. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which can be decoded with accuracy exceeding 99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into some of the benefits and limitations of small-molecule chemical information systems.