A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Sequence-dependent mechanochemical coupling of helicase translocation and unwinding at single-nucleotide resolution.

We used single-molecule picometer-resolution nanopore tweezers (SPRNT) to resolve the millisecond single-nucleotide steps of superfamily 1 helicase PcrA as it translocates on, or unwinds, several kilobase-long DNA molecules. We recorded more than two million enzyme steps under various assisting and opposing forces in diverse adenosine tri- and diphosphate conditions to comprehensively explore the mechanochemistry of PcrA motion. Forces applied in SPRNT mimic forces and physical barriers PcrA experiences in vivo, such as when the helicase encounters bound proteins or duplex DNA. We show how PcrA's kinetics change with such stimuli. SPRNT allows for direct association of the underlying DNA sequence with observed enzyme kinetics. Our data reveal that the underlying DNA sequence passing through the helicase strongly influences the kinetics during translocation and unwinding. Surprisingly, unwinding kinetics are not solely dominated by the base pairs being unwound. Instead, the sequence of the single-stranded DNA on which the PcrA walks determines much of the kinetics of unwinding.

Determining the effects of DNA sequence on Hel308 helicase translocation along single-stranded DNA using nanopore tweezers.

Motor enzymes that process nucleic-acid substrates play vital roles in all aspects of genome replication, expression, and repair. The DNA and RNA nucleobases are known to affect the kinetics of these systems in biologically meaningful ways. Recently, it was shown that DNA bases control the translocation speed of helicases on single-stranded DNA, however the cause of these effects remains unclear. We use single-molecule picometer-resolution nanopore tweezers (SPRNT) to measure the kinetics of translocation along single-stranded DNA by the helicase Hel308 from Thermococcus gammatolerans. SPRNT can measure enzyme steps with subangstrom resolution on millisecond timescales while simultaneously measuring the absolute position of the enzyme along the DNA substrate. Previous experiments with SPRNT revealed the presence of two distinct substates within the Hel308 ATP hydrolysis cycle, one [ATP]-dependent and the other [ATP]-independent. Here, we analyze in-depth the apparent sequence dependent behavior of the [ATP]-independent step. We find that DNA bases at two sites within Hel308 control sequence-specific kinetics of the [ATP]-independent step. We suggest mechanisms for the observed sequence-specific translocation kinetics. Similar SPRNT measurements and methods can be applied to other nucleic-acid-processing motor enzymes.

Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage.

Nanopore DNA sequencing is limited by low base-calling accuracy. Improved base-calling accuracy has so far relied on specialized base-calling algorithms, different nanopores and motor enzymes, or biochemical methods to re-read DNA molecules. Two primary error modes hamper sequencing accuracy: enzyme mis-steps and sequences with indistinguishable signals. We vary the driving voltage from 100 to 200 mV, with a frequency of 200 Hz, across a Mycobacterium smegmatis porin A (MspA) nanopore, thus changing how the DNA strand moves through the nanopore. A DNA helicase moves the DNA through the nanopore in discrete steps, and the variable voltage moves the DNA continuously between these steps. The electronic signal produced with variable voltage is used to overcome the primary error modes in base calling. We found that single-passage de novo base-calling accuracy of 62.7 ± 0.5% with a constant driving voltage improves to 79.3 ± 0.3% with a variable driving voltage. The variable-voltage sequencing mode is complementary to other methods to boost the accuracy of nanopore sequencing and could be incorporated into any enzyme-actuated nanopore sequencing device.