RT Journal Article SR Electronic T1 BigStitcher: Reconstructing high-resolution image datasets of cleared and expanded samples JF bioRxiv FD Cold Spring Harbor Laboratory SP 343954 DO 10.1101/343954 A1 David Hörl A1 Fabio Rojas Rusak A1 Friedrich Preusser A1 Paul Tillberg A1 Nadine Randel A1 Raghav K. Chhetri A1 Albert Cardona A1 Philipp J. Keller A1 Hartmann Harz A1 Heinrich Leonhardt A1 Mathias Treier A1 Stephan Preibisch YR 2018 UL http://biorxiv.org/content/early/2018/06/10/343954.abstract AB New methods for clearing and expansion of biological objects create large, transparent samples that can be rapidly imaged using light-sheet microscopy. Resulting image acquisitions are terabytes in size and consist of many large, unaligned image tiles that suffer from optical distortions. We developed the BigStitcher software that efficiently handles and reconstructs large multi-tile, multi-view acquisitions compensating all major optical effects, thereby making single-cell resolved whole-organ datasets amenable to biological studies.